Data can be imported from many different sources. In this exercise, we import data from:
library() function.library(ggplot2)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(gapminder)
library(gt)For this analysis we’ll use the Gapminder dataset from the gapminder R package.
head(gapminder)# A tibble: 6 × 6
country continent year lifeExp pop gdpPercap
<fct> <fct> <int> <dbl> <int> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779.
2 Afghanistan Asia 1957 30.3 9240934 821.
3 Afghanistan Asia 1962 32.0 10267083 853.
4 Afghanistan Asia 1967 34.0 11537966 836.
5 Afghanistan Asia 1972 36.1 13079460 740.
6 Afghanistan Asia 1977 38.4 14880372 786.The table below shows a summary for the population grouped by continent.
gapminder |>
filter(year == 2007) |>
group_by(continent) |>
summarise(
mean_life_exp = mean(pop)
) |>
gt() | continent | mean_life_exp |
|---|---|
| Africa | 17875763 |
| Americas | 35954847 |
| Asia | 115513752 |
| Europe | 19536618 |
| Oceania | 12274974 |
gapminder_2007 <- gapminder |>
filter(year == 2007)
ggplot(data = gapminder_2007,
mapping = aes(x = continent,
y = lifeExp)) +
geom_boxplot() +
geom_jitter(width = 0.1, alpha = 1/4, size = 3) +
labs(x = NULL,
y = "life expectancy") +
theme_minimal() 
library(ggplot2)
library(ggthemes)
library(ggridges)
library(palmerpenguins)head(penguins)# A tibble: 6 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Adelie Torgersen 39.1 18.7 181 3750
2 Adelie Torgersen 39.5 17.4 186 3800
3 Adelie Torgersen 40.3 18 195 3250
4 Adelie Torgersen NA NA NA NA
5 Adelie Torgersen 36.7 19.3 193 3450
6 Adelie Torgersen 39.3 20.6 190 3650
# ℹ 2 more variables: sex <fct>, year <int>str(penguins)tibble [344 × 8] (S3: tbl_df/tbl/data.frame)
$ species : Factor w/ 3 levels "Adelie","Chinstrap",..: 1 1 1 1 1 1 1 1 1 1 ...
$ island : Factor w/ 3 levels "Biscoe","Dream",..: 3 3 3 3 3 3 3 3 3 3 ...
$ bill_length_mm : num [1:344] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
$ bill_depth_mm : num [1:344] 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
$ flipper_length_mm: int [1:344] 181 186 195 NA 193 190 181 195 193 190 ...
$ body_mass_g : int [1:344] 3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 ...
$ sex : Factor w/ 2 levels "female","male": 2 1 1 NA 1 2 1 2 NA NA ...
$ year : int [1:344] 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...ggplot(data = penguins,
mapping = aes(x = flipper_length_mm,
y = body_mass_g)) +
geom_point()Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
ggplot(data = penguins,
mapping = aes(x = flipper_length_mm,
y = body_mass_g,
color = species,
shape = species)) +
geom_point()Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
ggplot(data = penguins,
mapping = aes(x = flipper_length_mm,
y = body_mass_g,
color = species,
shape = species)) +
geom_point(size = 5, alpha = 0.7)Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
ggplot(data = penguins,
mapping = aes(x = flipper_length_mm,
y = body_mass_g,
color = species,
shape = species)) +
geom_point(size = 5, alpha = 0.7) +
scale_color_colorblind() Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
ggplot(data = penguins,
mapping = aes(x = flipper_length_mm,
y = body_mass_g,
color = species,
shape = species)) +
geom_point(size = 5, alpha = 0.7) +
scale_color_manual(values = c("red", "blue", "green")) Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
Keyboard shortcut for the tilde (~) varies by keyboard layout:
ggplot(data = penguins,
mapping = aes(x = flipper_length_mm,
y = body_mass_g)) +
geom_point() +
facet_grid(species ~ island)Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
ggplot(data = penguins,
mapping = aes(x = flipper_length_mm,
y = body_mass_g)) +
geom_point() +
facet_wrap(~species)Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
Some code in this section is already prepared, we will add more code together.
ggplot(data = penguins,
mapping = aes(x = flipper_length_mm,
y = body_mass_g,
color = species,
shape = species)) +
geom_point(size = 5, alpha = 0.7) +
scale_color_colorblind() +
theme_minimal()Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
ggplot(data = penguins,
mapping = aes(x = species)) +
geom_bar()
ggplot(data = penguins,
mapping = aes(x = species,
fill = island)) +
geom_bar()
The code in this section is already prepared, we will run through the code chunks together.
ggplot(data = penguins,
mapping = aes(x = body_mass_g)) +
geom_histogram()`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_bin()`).
ggplot(data = penguins,
mapping = aes(x = body_mass_g,
fill = species)) +
geom_histogram()`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_bin()`).
ggplot(data = penguins,
mapping = aes(x = body_mass_g,
fill = species)) +
geom_density()Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_density()`).
ggplot(data = penguins,
mapping = aes(x = body_mass_g,
y = species,
fill = species)) +
geom_density_ridges()Picking joint bandwidth of 153Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_density_ridges()`).
library(ggplot2)
library(dplyr)
library(gapminder)head(gapminder)# A tibble: 6 × 6
country continent year lifeExp pop gdpPercap
<fct> <fct> <int> <dbl> <int> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779.
2 Afghanistan Asia 1957 30.3 9240934 821.
3 Afghanistan Asia 1962 32.0 10267083 853.
4 Afghanistan Asia 1967 34.0 11537966 836.
5 Afghanistan Asia 1972 36.1 13079460 740.
6 Afghanistan Asia 1977 38.4 14880372 786.tail(gapminder)# A tibble: 6 × 6
country continent year lifeExp pop gdpPercap
<fct> <fct> <int> <dbl> <int> <dbl>
1 Zimbabwe Africa 1982 60.4 7636524 789.
2 Zimbabwe Africa 1987 62.4 9216418 706.
3 Zimbabwe Africa 1992 60.4 10704340 693.
4 Zimbabwe Africa 1997 46.8 11404948 792.
5 Zimbabwe Africa 2002 40.0 11926563 672.
6 Zimbabwe Africa 2007 43.5 12311143 470.glimpse(gapminder)Rows: 1,704
Columns: 6
$ country <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", …
$ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, …
$ year <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, …
$ lifeExp <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.8…
$ pop <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12…
$ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, …str(gapminder)tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
$ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
$ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
$ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
$ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
$ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
$ gdpPercap: num [1:1704] 779 821 853 836 740 ...nrow(gapminder)[1] 1704ncol(gapminder)[1] 6gapminder_2007 <- gapminder |>
filter(year == 2007)ggplot(data = gapminder_2007,
mapping = aes(x = continent,
y = lifeExp)) +
geom_boxplot()
The required packages for this homework exercise have already been added.
library(gapminder)
library(ggplot2)
library(dplyr)Below is a typical task description as you will find them in the homework assignments. For “Fill in the gaps” tasks, you should replace the underscores ___ with the described code and then change the value of the code block option from false to true. In other tasks, you will create your own code from scratch. Over time, the task descriptions will be become less detailed.
Fill in the gaps
A code chunk has already been created below.
Start with the gapminder object and add the pipe operator at the end of the line.
On a new line use the filter() function to narrow down the data for observation of the year 2007.
Use the assignment operator to assign the data to an object named gapminder_2007.
Run the code contained in the code chunk and fix any errors.
Next to the code chunk option #| eval: change the value from false to true.
Render the document and fix any errors.
gapminder_2007 <- gapminder |>
filter(year == 2007) This is a typical task without any starter code.
Add a new code chunk below point 5.
Use the ggplot() function and the gapminder_2007 object to create a boxplot with the following aesthetic mappings:
fill = continent argument inside aes()Run the code contained in the code chunk and fix any errors.
What are the data types of the three variables used for aesthetic mappings?
ggplot(data = gapminder_2007,
mapping = aes(x = continent,
y = lifeExp,
fill = continent)) +
geom_boxplot()
#Assignment 02: Data Visualisation
The required packages for this homework exercise have already been added.
library(gapminder)
library(ggplot2)
library(dplyr)
library(readr)
library(sf)Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1; sf_use_s2() is TRUElibrary(rnaturalearth)Fill in the gaps
A code chunk has already been created below.
Start with the gapminder object and add the pipe operator at the end of the line.
On a new line use the filter() function to narrow down the data for observation of the year 2007.
Use the assignment operator to assign the data to an object named gapminder_2007.
Run the code contained in the code chunk and fix any errors.
Next to the code chunk option #| eval: change the value from false to true.
Render the document and fix any errors.
gapminder_2007 <- gapminder |>
filter(year == 2007) Fill in the gaps
A code chunk has already been created.
Start with the gapminder_2007 object and add the pipe operator at the end of the line.
On a new line use the group_by() function to group the operations that follow by continent. Add the pipe operator at the end of the line.
On a new line use the summarise() function to calculate the number of observations (count) and median life expectancy.
Use the assignment operator to assign the data to an object named gapminder_summary_2007.
Run the code contained in the code chunk and fix any errors.
Next to the code chunk option #| eval: change the value from false to true.
Render the document and fix any errors.
gapminder_summary_2007 <- gapminder_2007 |>
group_by(continent) |>
summarise(
count = n(),
lifeExp = median(lifeExp)
)Fill in the gaps
A code chunk has already been created.
Start with the gapminder object and add the pipe operator at the end of the line.
On a new line use the group_by() function to group the operations that follow by continent and year. Add the pipe operator at the end of the line.
On a new line use the summarise() function to calculate and median life expectancy.
Use the assignment operator to assign the data to an object named gapminder_summary_continent_year
Run the code contained in the code chunk and fix any errors.
Next to the code chunk option #| eval: change the value from false to true.
Render the document and fix any errors.
gapminder_summary_continent_year <- gapminder |>
group_by(continent, year) |>
summarise(lifeExp = median(lifeExp)) `summarise()` has grouped output by 'continent'. You can override using the
`.groups` argument.Thank you for working through the previous tasks. We are convinced that you have done a great job, but because the task descriptions aren’t always unambiguous, we have imported the data that we would have expected to be created and stored in the objects gapminder_2007, gapminder_summary_2007 and gapminder_summary_continent_year at the previous code chunks. This is to ensure that you can work through the following tasks.
gapminder_2007 <- read_rds(here::here("/cloud/project/data/gapminder-2007.rds"))
gapminder_summary_2007 <- read_rds(here::here("/cloud/project/data/gapminder-summary-2007.rds"))
gapminder_summary_continent_year <- read_rds(here::here("/cloud/project/data/gapminder-summary-continent-year.rds"))A code chunk has already been created.
Use the ggplot() function and the gapminder_2007 object to create a boxplot with the following aesthetic mappings:
fill = continent argument inside aes()Do not display (ignore) the outliers in the plot. Note: Use a search engine or an AI tool to find the solution and add the link to the solution you have found.
Run the code contained in the code chunk and fix any errors.
What are the data types of the three variables used for aesthetic mappings?
ggplot(data = gapminder_2007,
mapping = aes(x = continent,
y = lifeExp,
fill = continent)) +
geom_boxplot(outlier.shape = NA) 
A code chunk has already been created.
Use the ggplot() function and the gapminder_summary_continent_year object to create a connected scatterplot (also called timeseries plot) using the geom_line() and geom_point()functions with the following aesthetic mappings:
color = continent argument inside aes()ggplot(data = gapminder_summary_continent_year,
mapping = aes(x = year,
y = lifeExp,
color = continent)) +
geom_line() +
geom_point() 
geom_col()A code chunk has already been created.
Use the ggplot() function and the gapminder_summary_2007 object to create a barplot using the geom_col() function with the following aesthetic mappings:
ggplot(data = gapminder_summary_2007,
mapping = aes(x = continent,
y = count)) +
geom_col()
geom_bar()A code chunk has already been created.
Use the ggplot() function and the gapminder_2007 object to create a barplot using the geom_bar() function with the following aesthetic mappings:
Run the code contained in the code chunk and fix any errors.
The plot is identical to the plot created with geom_col(). Why? What does the geom_bar() function do? Write your text here:
ggplot(data = gapminder_2007,
mapping = aes(x = continent)) +
geom_bar()
A code chunk has already been created.
Use the ggplot() function and the gapminder_2007 object to create a histogram using the geom_histogram() function with the following aesthetic mappings:
fill = continent argument inside aes()Run the code contained in the code chunk and fix any errors.
Inside the geom_histogram() function, add the following arguments and values:
col = "grey30"breaks = seq(40, 85, 2.5)Run the code contained in the code chunk and fix any errors.
Describe how the geom_histogram() function is similar to the geom_bar() function.
What happens by adding the ‘breaks’ argument? Play around with the numbers inside of seq() to see what changes. Describe here what you observe:
ggplot(data = gapminder_2007,
mapping = aes(x = lifeExp,
fill = continent)) +
geom_histogram(col = "grey30", breaks = seq(40, 85, 2.5)) 
A code chunk has already been created.
Use the ggplot() function and assign gapminder_2007 and create a scatterplot using the geom_point()function with the following aesthetic mappings:
color = continent argument inside aes()Run the code contained in the code chunk and fix any errors.
Use the variable continent to facet the plot by adding: facet_wrap(~continent).
Run the code contained in the code chunk and fix any errors.
ggplot(data = gapminder_2007,
mapping = aes(x = gdpPercap,
y = lifeExp,
size = pop,
color = country)) +
geom_point(show.legend = FALSE) +
facet_wrap(~continent) 
A code chunk with complete code has already been prepared.
Run the code contained in the code chunk and fix any errors.
Remove the ‘#’ sign at the line that starts with the scale_color_manual() function
What is stored in the country_colors object? Find out by executing the object in the Console (type it to the Console and hit enter). Do the same again, but with a question mark ?country_colors.
Next to the code chunk option #| eval: change the value from false to true.
Render the document and fix any errors.
ggplot(data = gapminder,
mapping = aes(x = year,
y = lifeExp,
group = country,
color = country)) +
geom_line(lwd = 1, show.legend = FALSE) +
facet_wrap(~continent) +
# scale_color_manual(values = country_colors) +
theme_minimal() 
You can also prepare maps with ggplot2. It’s beyond the scope of the class to teach you the foundations of spatial data in R, but a popular package to work with spatial data is the sf (Simple Features) R Package. The rnaturalearth R Package facilitates world mapping by making Natural Earth map data more easily available to R users.
The code chunk below contains code for a world map that shows countries by income group. To view the map, do the following:
Run the code contained in the code chunk and fix any errors.
Next to the code chunk option #| eval: change the value from false to true.
Render the document and fix any errors.
world <- ne_countries(scale = "small", returnclass = "sf")
world |>
mutate(income_grp = factor(income_grp, ordered = T)) |>
ggplot(aes(fill = income_grp)) +
geom_sf() +
theme_void() +
theme(legend.position = "top") +
labs(fill = "Income Group:") +
guides(fill = guide_legend(nrow = 2, byrow = TRUE))
The code for the code chunk is taken from here: More here: https://bookdown.org/alhdzsz/data_viz_ir/maps.html
If you are interested in working with spatial data in R, then we recommend the following resources for further study:
library(readr)
library(dplyr)In this exercise we use data of the UNICEF/WHO Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (WASH). The data is available at https://washdata.org/data and published as an R data package at https://github.com/WASHNote/jmpwashdata/.
The data set is available in the data folder as a CSV file named jmp_wld_sanitation_long.csv.
The data set contains the following variables:
name: country nameiso3: ISO3 country codeyear: year of observationregion_sdg: SDG regionresidence: residence type (national, rural, urban)varname_short: short variable name (JMP naming convention)varname_long: long variable name (JMP naming convention)We use the read_csv() function to import the data set into R.
sanitation <- read_csv("/cloud/project/data/jmp_wld_sanitation_long.csv")Rows: 73710 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (6): name, iso3, region_sdg, varname_short, varname_long, residence
dbl (2): year, percent
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.sanitation# A tibble: 73,710 × 8
name iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 Afghanis… AFG 2000 Central a… san_bas basic sanit… national 21.9
2 Afghanis… AFG 2000 Central a… san_bas basic sanit… rural 19.3
3 Afghanis… AFG 2000 Central a… san_bas basic sanit… urban 30.9
4 Afghanis… AFG 2000 Central a… san_lim limited san… national 5.65
5 Afghanis… AFG 2000 Central a… san_lim limited san… rural 3.14
6 Afghanis… AFG 2000 Central a… san_lim limited san… urban 14.5
7 Afghanis… AFG 2000 Central a… san_unimp unimproved … national 46.7
8 Afghanis… AFG 2000 Central a… san_unimp unimproved … rural 46.3
9 Afghanis… AFG 2000 Central a… san_unimp unimproved … urban 48.1
10 Afghanis… AFG 2000 Central a… san_od no sanitati… national 25.8
# ℹ 73,700 more rowsglimpse(sanitation)Rows: 73,710
Columns: 8
$ name <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanista…
$ iso3 <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", …
$ year <dbl> 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 20…
$ region_sdg <chr> "Central and Southern Asia", "Central and Southern Asia"…
$ varname_short <chr> "san_bas", "san_bas", "san_bas", "san_lim", "san_lim", "…
$ varname_long <chr> "basic sanitation services", "basic sanitation services"…
$ residence <chr> "national", "rural", "urban", "national", "rural", "urba…
$ percent <dbl> 21.870802, 19.322798, 30.863719, 5.648528, 3.136148, 14.…The dplyr R Package aims to provide a function for each basic verb of data manipulation. These verbs can be organised into three categories based on the component of the dataset that they work with:
The function filter() chooses rows based on column values. To use filtering effectively, you have to know how to select the observations that you want using the comparison operators.
R provides the standard suite: >, >=, <, <=, != (not equal), and == (equal).
sanitation |>
filter(residence == "national")# A tibble: 24,570 × 8
name iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 Afghanis… AFG 2000 Central a… san_bas basic sanit… national 21.9
2 Afghanis… AFG 2000 Central a… san_lim limited san… national 5.65
3 Afghanis… AFG 2000 Central a… san_unimp unimproved … national 46.7
4 Afghanis… AFG 2000 Central a… san_od no sanitati… national 25.8
5 Afghanis… AFG 2000 Central a… san_sm safely mana… national NA
6 Afghanis… AFG 2001 Central a… san_bas basic sanit… national 21.9
7 Afghanis… AFG 2001 Central a… san_lim limited san… national 5.66
8 Afghanis… AFG 2001 Central a… san_unimp unimproved … national 46.7
9 Afghanis… AFG 2001 Central a… san_od no sanitati… national 25.8
10 Afghanis… AFG 2001 Central a… san_sm safely mana… national NA
# ℹ 24,560 more rowssanitation |>
filter(residence != "national")# A tibble: 49,140 × 8
name iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 Afghanis… AFG 2000 Central a… san_bas basic sanit… rural 19.3
2 Afghanis… AFG 2000 Central a… san_bas basic sanit… urban 30.9
3 Afghanis… AFG 2000 Central a… san_lim limited san… rural 3.14
4 Afghanis… AFG 2000 Central a… san_lim limited san… urban 14.5
5 Afghanis… AFG 2000 Central a… san_unimp unimproved … rural 46.3
6 Afghanis… AFG 2000 Central a… san_unimp unimproved … urban 48.1
7 Afghanis… AFG 2000 Central a… san_od no sanitati… rural 31.3
8 Afghanis… AFG 2000 Central a… san_od no sanitati… urban 6.51
9 Afghanis… AFG 2000 Central a… san_sm safely mana… rural NA
10 Afghanis… AFG 2000 Central a… san_sm safely mana… urban NA
# ℹ 49,130 more rowssanitation |>
filter(residence == "national", iso3 == "SEN") # A tibble: 105 × 8
name iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 Senegal SEN 2000 Sub-Saharan… san_bas basic sanit… national 37.5
2 Senegal SEN 2000 Sub-Saharan… san_lim limited san… national 10.8
3 Senegal SEN 2000 Sub-Saharan… san_unimp unimproved … national 27.4
4 Senegal SEN 2000 Sub-Saharan… san_od no sanitati… national 24.4
5 Senegal SEN 2000 Sub-Saharan… san_sm safely mana… national 14.0
6 Senegal SEN 2001 Sub-Saharan… san_bas basic sanit… national 38.4
7 Senegal SEN 2001 Sub-Saharan… san_lim limited san… national 11.0
8 Senegal SEN 2001 Sub-Saharan… san_unimp unimproved … national 26.8
9 Senegal SEN 2001 Sub-Saharan… san_od no sanitati… national 23.7
10 Senegal SEN 2001 Sub-Saharan… san_sm safely mana… national 14.3
# ℹ 95 more rowssanitation |>
filter(iso3 == "UGA" | iso3 == "PER" | iso3 == "IND") # A tibble: 945 × 8
name iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 India IND 2000 Central and S… san_bas basic sanit… national 15.0
2 India IND 2000 Central and S… san_bas basic sanit… rural 2.25
3 India IND 2000 Central and S… san_bas basic sanit… urban 48.4
4 India IND 2000 Central and S… san_lim limited san… national 5.15
5 India IND 2000 Central and S… san_lim limited san… rural 0.515
6 India IND 2000 Central and S… san_lim limited san… urban 17.3
7 India IND 2000 Central and S… san_unimp unimproved … national 5.73
8 India IND 2000 Central and S… san_unimp unimproved … rural 5.00
9 India IND 2000 Central and S… san_unimp unimproved … urban 7.65
10 India IND 2000 Central and S… san_od no sanitati… national 74.1
# ℹ 935 more rowssanitation |>
filter(iso3 %in% c("UGA", "PER", "IND"))# A tibble: 945 × 8
name iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 India IND 2000 Central and S… san_bas basic sanit… national 15.0
2 India IND 2000 Central and S… san_bas basic sanit… rural 2.25
3 India IND 2000 Central and S… san_bas basic sanit… urban 48.4
4 India IND 2000 Central and S… san_lim limited san… national 5.15
5 India IND 2000 Central and S… san_lim limited san… rural 0.515
6 India IND 2000 Central and S… san_lim limited san… urban 17.3
7 India IND 2000 Central and S… san_unimp unimproved … national 5.73
8 India IND 2000 Central and S… san_unimp unimproved … rural 5.00
9 India IND 2000 Central and S… san_unimp unimproved … urban 7.65
10 India IND 2000 Central and S… san_od no sanitati… national 74.1
# ℹ 935 more rowssanitation |>
filter(percent > 80)# A tibble: 8,314 × 8
name iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 Albania ALB 2000 Northern Am… san_bas basic sanit… national 89.5
2 Albania ALB 2000 Northern Am… san_bas basic sanit… rural 84.2
3 Albania ALB 2000 Northern Am… san_bas basic sanit… urban 96.9
4 Albania ALB 2001 Northern Am… san_bas basic sanit… national 90.0
5 Albania ALB 2001 Northern Am… san_bas basic sanit… rural 84.9
6 Albania ALB 2001 Northern Am… san_bas basic sanit… urban 97.0
7 Albania ALB 2002 Northern Am… san_bas basic sanit… national 90.6
8 Albania ALB 2002 Northern Am… san_bas basic sanit… rural 85.7
9 Albania ALB 2002 Northern Am… san_bas basic sanit… urban 97.0
10 Albania ALB 2003 Northern Am… san_bas basic sanit… national 91.2
# ℹ 8,304 more rowssanitation |>
filter(percent <= 5)# A tibble: 21,424 × 8
name iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 Afghanis… AFG 2000 Central a… san_lim limited san… rural 3.14
2 Afghanis… AFG 2001 Central a… san_lim limited san… rural 3.14
3 Afghanis… AFG 2002 Central a… san_lim limited san… rural 3.35
4 Afghanis… AFG 2003 Central a… san_lim limited san… rural 3.57
5 Afghanis… AFG 2004 Central a… san_lim limited san… rural 3.79
6 Afghanis… AFG 2005 Central a… san_lim limited san… rural 4.01
7 Afghanis… AFG 2005 Central a… san_od no sanitati… urban 4.86
8 Afghanis… AFG 2006 Central a… san_lim limited san… rural 4.22
9 Afghanis… AFG 2006 Central a… san_od no sanitati… urban 4.45
10 Afghanis… AFG 2007 Central a… san_lim limited san… rural 4.44
# ℹ 21,414 more rowssanitation |>
filter(is.na(percent))# A tibble: 19,743 × 8
name iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 Afghanis… AFG 2000 Central a… san_sm safely mana… national NA
2 Afghanis… AFG 2000 Central a… san_sm safely mana… rural NA
3 Afghanis… AFG 2000 Central a… san_sm safely mana… urban NA
4 Afghanis… AFG 2001 Central a… san_sm safely mana… national NA
5 Afghanis… AFG 2001 Central a… san_sm safely mana… rural NA
6 Afghanis… AFG 2001 Central a… san_sm safely mana… urban NA
7 Afghanis… AFG 2002 Central a… san_sm safely mana… national NA
8 Afghanis… AFG 2002 Central a… san_sm safely mana… rural NA
9 Afghanis… AFG 2002 Central a… san_sm safely mana… urban NA
10 Afghanis… AFG 2003 Central a… san_sm safely mana… national NA
# ℹ 19,733 more rowssanitation |>
filter(!is.na(percent))# A tibble: 53,967 × 8
name iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 Afghanis… AFG 2000 Central a… san_bas basic sanit… national 21.9
2 Afghanis… AFG 2000 Central a… san_bas basic sanit… rural 19.3
3 Afghanis… AFG 2000 Central a… san_bas basic sanit… urban 30.9
4 Afghanis… AFG 2000 Central a… san_lim limited san… national 5.65
5 Afghanis… AFG 2000 Central a… san_lim limited san… rural 3.14
6 Afghanis… AFG 2000 Central a… san_lim limited san… urban 14.5
7 Afghanis… AFG 2000 Central a… san_unimp unimproved … national 46.7
8 Afghanis… AFG 2000 Central a… san_unimp unimproved … rural 46.3
9 Afghanis… AFG 2000 Central a… san_unimp unimproved … urban 48.1
10 Afghanis… AFG 2000 Central a… san_od no sanitati… national 25.8
# ℹ 53,957 more rowsStoring a resulting data frame as a new object
sanitation_national_2020_sm <- sanitation |>
filter(residence == "national",
year == 2020,
varname_short == "san_sm")The function arrange() changes the order of the rows.
sanitation_national_2020_sm |>
arrange(percent)# A tibble: 234 × 8
name iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 Ethiopia ETH 2020 Sub-Sahar… san_sm safely mana… national 6.68
2 Togo TGO 2020 Sub-Sahar… san_sm safely mana… national 9.13
3 Chad TCD 2020 Sub-Sahar… san_sm safely mana… national 10.1
4 Madagasc… MDG 2020 Sub-Sahar… san_sm safely mana… national 10.4
5 Guinea-B… GNB 2020 Sub-Sahar… san_sm safely mana… national 12.2
6 North Ma… MKD 2020 Northern … san_sm safely mana… national 12.2
7 Democrat… COD 2020 Sub-Sahar… san_sm safely mana… national 12.7
8 Ghana GHA 2020 Sub-Sahar… san_sm safely mana… national 13.3
9 Central … CAF 2020 Sub-Sahar… san_sm safely mana… national 13.6
10 Sierra L… SLE 2020 Sub-Sahar… san_sm safely mana… national 14.0
# ℹ 224 more rowssanitation_national_2020_sm |>
arrange(desc(percent))# A tibble: 234 × 8
name iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 Andorra AND 2020 Northern … san_sm safely mana… national 100.
2 Kuwait KWT 2020 Western A… san_sm safely mana… national 100
3 Monaco MCO 2020 Northern … san_sm safely mana… national 100
4 Singapore SGP 2020 Eastern a… san_sm safely mana… national 100
5 Republic… KOR 2020 Eastern a… san_sm safely mana… national 99.9
6 Switzerl… CHE 2020 Northern … san_sm safely mana… national 99.7
7 Austria AUT 2020 Northern … san_sm safely mana… national 99.6
8 United A… ARE 2020 Western A… san_sm safely mana… national 99.2
9 Liechten… LIE 2020 Northern … san_sm safely mana… national 98.8
10 United S… USA 2020 Northern … san_sm safely mana… national 98.3
# ℹ 224 more rowsThe select() function chooses columns based on their names.
sanitation_national_2020_sm |>
select(name, percent)# A tibble: 234 × 2
name percent
<chr> <dbl>
1 Afghanistan NA
2 Albania 47.7
3 Algeria 17.6
4 American Samoa NA
5 Andorra 100.
6 Angola NA
7 Anguilla NA
8 Antigua and Barbuda NA
9 Argentina NA
10 Armenia 69.3
# ℹ 224 more rowssanitation_national_2020_sm |>
select(-varname_short)# A tibble: 234 × 7
name iso3 year region_sdg varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <dbl>
1 Afghanistan AFG 2020 Central and S… safely mana… national NA
2 Albania ALB 2020 Northern Amer… safely mana… national 47.7
3 Algeria DZA 2020 Western Asia … safely mana… national 17.6
4 American Samoa ASM 2020 Oceania safely mana… national NA
5 Andorra AND 2020 Northern Amer… safely mana… national 100.
6 Angola AGO 2020 Sub-Saharan A… safely mana… national NA
7 Anguilla AIA 2020 Latin America… safely mana… national NA
8 Antigua and Barbuda ATG 2020 Latin America… safely mana… national NA
9 Argentina ARG 2020 Latin America… safely mana… national NA
10 Armenia ARM 2020 Western Asia … safely mana… national 69.3
# ℹ 224 more rowssanitation_national_2020_sm |>
select(name:region_sdg, percent)# A tibble: 234 × 5
name iso3 year region_sdg percent
<chr> <chr> <dbl> <chr> <dbl>
1 Afghanistan AFG 2020 Central and Southern Asia NA
2 Albania ALB 2020 Northern America and Europe 47.7
3 Algeria DZA 2020 Western Asia and Northern Africa 17.6
4 American Samoa ASM 2020 Oceania NA
5 Andorra AND 2020 Northern America and Europe 100.
6 Angola AGO 2020 Sub-Saharan Africa NA
7 Anguilla AIA 2020 Latin America and the Caribbean NA
8 Antigua and Barbuda ATG 2020 Latin America and the Caribbean NA
9 Argentina ARG 2020 Latin America and the Caribbean NA
10 Armenia ARM 2020 Western Asia and Northern Africa 69.3
# ℹ 224 more rowsThe rename() function changes the names of variables.
sanitation |>
rename(country = name)# A tibble: 73,710 × 8
country iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 Afghanis… AFG 2000 Central a… san_bas basic sanit… national 21.9
2 Afghanis… AFG 2000 Central a… san_bas basic sanit… rural 19.3
3 Afghanis… AFG 2000 Central a… san_bas basic sanit… urban 30.9
4 Afghanis… AFG 2000 Central a… san_lim limited san… national 5.65
5 Afghanis… AFG 2000 Central a… san_lim limited san… rural 3.14
6 Afghanis… AFG 2000 Central a… san_lim limited san… urban 14.5
7 Afghanis… AFG 2000 Central a… san_unimp unimproved … national 46.7
8 Afghanis… AFG 2000 Central a… san_unimp unimproved … rural 46.3
9 Afghanis… AFG 2000 Central a… san_unimp unimproved … urban 48.1
10 Afghanis… AFG 2000 Central a… san_od no sanitati… national 25.8
# ℹ 73,700 more rowsThe mutate() function adds new variables based on existing variables or external data.
sanitation |>
mutate(prop = percent / 100)# A tibble: 73,710 × 9
name iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 Afghanis… AFG 2000 Central a… san_bas basic sanit… national 21.9
2 Afghanis… AFG 2000 Central a… san_bas basic sanit… rural 19.3
3 Afghanis… AFG 2000 Central a… san_bas basic sanit… urban 30.9
4 Afghanis… AFG 2000 Central a… san_lim limited san… national 5.65
5 Afghanis… AFG 2000 Central a… san_lim limited san… rural 3.14
6 Afghanis… AFG 2000 Central a… san_lim limited san… urban 14.5
7 Afghanis… AFG 2000 Central a… san_unimp unimproved … national 46.7
8 Afghanis… AFG 2000 Central a… san_unimp unimproved … rural 46.3
9 Afghanis… AFG 2000 Central a… san_unimp unimproved … urban 48.1
10 Afghanis… AFG 2000 Central a… san_od no sanitati… national 25.8
# ℹ 73,700 more rows
# ℹ 1 more variable: prop <dbl>sanitation |>
mutate(id = seq(1:n()))# A tibble: 73,710 × 9
name iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 Afghanis… AFG 2000 Central a… san_bas basic sanit… national 21.9
2 Afghanis… AFG 2000 Central a… san_bas basic sanit… rural 19.3
3 Afghanis… AFG 2000 Central a… san_bas basic sanit… urban 30.9
4 Afghanis… AFG 2000 Central a… san_lim limited san… national 5.65
5 Afghanis… AFG 2000 Central a… san_lim limited san… rural 3.14
6 Afghanis… AFG 2000 Central a… san_lim limited san… urban 14.5
7 Afghanis… AFG 2000 Central a… san_unimp unimproved … national 46.7
8 Afghanis… AFG 2000 Central a… san_unimp unimproved … rural 46.3
9 Afghanis… AFG 2000 Central a… san_unimp unimproved … urban 48.1
10 Afghanis… AFG 2000 Central a… san_od no sanitati… national 25.8
# ℹ 73,700 more rows
# ℹ 1 more variable: id <int>sanitation |>
mutate(id = 1:n()) |>
relocate(id)# A tibble: 73,710 × 9
id name iso3 year region_sdg varname_short varname_long residence
<int> <chr> <chr> <dbl> <chr> <chr> <chr> <chr>
1 1 Afghanistan AFG 2000 Central a… san_bas basic sanit… national
2 2 Afghanistan AFG 2000 Central a… san_bas basic sanit… rural
3 3 Afghanistan AFG 2000 Central a… san_bas basic sanit… urban
4 4 Afghanistan AFG 2000 Central a… san_lim limited san… national
5 5 Afghanistan AFG 2000 Central a… san_lim limited san… rural
6 6 Afghanistan AFG 2000 Central a… san_lim limited san… urban
7 7 Afghanistan AFG 2000 Central a… san_unimp unimproved … national
8 8 Afghanistan AFG 2000 Central a… san_unimp unimproved … rural
9 9 Afghanistan AFG 2000 Central a… san_unimp unimproved … urban
10 10 Afghanistan AFG 2000 Central a… san_od no sanitati… national
# ℹ 73,700 more rows
# ℹ 1 more variable: percent <dbl>sanitation |>
mutate(id = 1:n()) |>
relocate(id, .before = name)# A tibble: 73,710 × 9
id name iso3 year region_sdg varname_short varname_long residence
<int> <chr> <chr> <dbl> <chr> <chr> <chr> <chr>
1 1 Afghanistan AFG 2000 Central a… san_bas basic sanit… national
2 2 Afghanistan AFG 2000 Central a… san_bas basic sanit… rural
3 3 Afghanistan AFG 2000 Central a… san_bas basic sanit… urban
4 4 Afghanistan AFG 2000 Central a… san_lim limited san… national
5 5 Afghanistan AFG 2000 Central a… san_lim limited san… rural
6 6 Afghanistan AFG 2000 Central a… san_lim limited san… urban
7 7 Afghanistan AFG 2000 Central a… san_unimp unimproved … national
8 8 Afghanistan AFG 2000 Central a… san_unimp unimproved … rural
9 9 Afghanistan AFG 2000 Central a… san_unimp unimproved … urban
10 10 Afghanistan AFG 2000 Central a… san_od no sanitati… national
# ℹ 73,700 more rows
# ℹ 1 more variable: percent <dbl>The summarise() function reduces multiple values down to a single summary.
sanitation_national_2020_sm |>
summarise()# A tibble: 1 × 0sanitation_national_2020_sm |>
summarise(mean_percent = mean(percent))# A tibble: 1 × 1
mean_percent
<dbl>
1 NAsanitation_national_2020_sm |>
summarise(mean_percent = mean(percent, na.rm = TRUE))# A tibble: 1 × 1
mean_percent
<dbl>
1 60.3sanitation_national_2020_sm |>
summarise(n = n(),
mean_percent = mean(percent, na.rm = TRUE))# A tibble: 1 × 2
n mean_percent
<int> <dbl>
1 234 60.3sanitation_national_2020_sm |>
filter(!is.na(percent)) |>
summarise(n = n(),
mean_percent = mean(percent),
sd_percent = sd(percent))# A tibble: 1 × 3
n mean_percent sd_percent
<int> <dbl> <dbl>
1 120 60.3 29.9The group_by() function is used to group the data by one or more variables.
sanitation_national_2020_sm |>
group_by(region_sdg)# A tibble: 234 × 8
# Groups: region_sdg [8]
name iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 Afghanis… AFG 2020 Central a… san_sm safely mana… national NA
2 Albania ALB 2020 Northern … san_sm safely mana… national 47.7
3 Algeria DZA 2020 Western A… san_sm safely mana… national 17.6
4 American… ASM 2020 Oceania san_sm safely mana… national NA
5 Andorra AND 2020 Northern … san_sm safely mana… national 100.
6 Angola AGO 2020 Sub-Sahar… san_sm safely mana… national NA
7 Anguilla AIA 2020 Latin Ame… san_sm safely mana… national NA
8 Antigua … ATG 2020 Latin Ame… san_sm safely mana… national NA
9 Argentina ARG 2020 Latin Ame… san_sm safely mana… national NA
10 Armenia ARM 2020 Western A… san_sm safely mana… national 69.3
# ℹ 224 more rowssanitation_national_2020_sm |>
group_by(region_sdg) |>
summarise(n = n(),
mean_percent = mean(percent),
sd_percent = sd(percent))# A tibble: 8 × 4
region_sdg n mean_percent sd_percent
<chr> <int> <dbl> <dbl>
1 Australia and New Zealand 2 78.2 5.61
2 Central and Southern Asia 14 NA NA
3 Eastern and South-Eastern Asia 18 NA NA
4 Latin America and the Caribbean 50 NA NA
5 Northern America and Europe 53 NA NA
6 Oceania 21 NA NA
7 Sub-Saharan Africa 51 NA NA
8 Western Asia and Northern Africa 25 NA NA sanitation_national_2020_sm |>
filter(!is.na(percent)) |>
group_by(region_sdg) |>
summarise(n = n(),
mean_percent = mean(percent),
sd_percent = sd(percent))# A tibble: 8 × 4
region_sdg n mean_percent sd_percent
<chr> <int> <dbl> <dbl>
1 Australia and New Zealand 2 78.2 5.61
2 Central and Southern Asia 5 58.2 21.5
3 Eastern and South-Eastern Asia 11 69.8 21.4
4 Latin America and the Caribbean 14 43.4 16.8
5 Northern America and Europe 44 81.9 19.9
6 Oceania 3 36.1 10.7
7 Sub-Saharan Africa 21 21.4 10.9
8 Western Asia and Northern Africa 20 62.7 29.5 The count() function is a convenient wrapper for group_by() and summarise(n = n()). You can prepare frequency tables with count().
sanitation |>
count(region_sdg)# A tibble: 8 × 2
region_sdg n
<chr> <int>
1 Australia and New Zealand 630
2 Central and Southern Asia 4410
3 Eastern and South-Eastern Asia 5670
4 Latin America and the Caribbean 15750
5 Northern America and Europe 16695
6 Oceania 6615
7 Sub-Saharan Africa 16065
8 Western Asia and Northern Africa 7875sanitation |>
count(varname_short)# A tibble: 5 × 2
varname_short n
<chr> <int>
1 san_bas 14742
2 san_lim 14742
3 san_od 14742
4 san_sm 14742
5 san_unimp 14742sanitation |>
count(varname_long)# A tibble: 5 × 2
varname_long n
<chr> <int>
1 basic sanitation services 14742
2 limited sanitation services 14742
3 no sanitation facilities 14742
4 safely managed sanitation services 14742
5 unimproved sanitation facilities 14742sanitation |>
count(varname_short, varname_long)# A tibble: 5 × 3
varname_short varname_long n
<chr> <chr> <int>
1 san_bas basic sanitation services 14742
2 san_lim limited sanitation services 14742
3 san_od no sanitation facilities 14742
4 san_sm safely managed sanitation services 14742
5 san_unimp unimproved sanitation facilities 14742#Module 03b: Filter function
library(readr)
library(dplyr)
library(ggplot2)
library(ggthemes)In this exercise we use data of the UNICEF/WHO Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (WASH). The data is available at https://washdata.org/data and published as an R data package at https://github.com/WASHNote/jmpwashdata/.
The data set jmp_wld_sanitation_long is available in the data folder of this repository. The data set is in long format and contains the following variables:
name: country nameiso3: ISO3 country codeyear: year of observationregion_sdg: SDG regionresidence: residence type (national, rural, urban)varname_short: short variable name (JMP naming convention)varname_long: long variable name (JMP naming convention)We use the read_csv() function to import the data set into R.
sanitation <- read_csv("/cloud/project/data/jmp_wld_sanitation_long.csv")Rows: 73710 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (6): name, iso3, region_sdg, varname_short, varname_long, residence
dbl (2): year, percent
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.filter() function to create a subset from the sanitation data containing national estimates for the year 2020.sanitation_national_2020sanitation_national_2020 <- sanitation |>
filter(residence == "national", year == 2020)filter() function to create a subset from the sanitation data containing urban and rural estimates for Nigeria.sanitation_nigeria_urban_ruralsanitation_nigeria_urban_rural <- sanitation |>
filter(name == "Nigeria", residence != "national")Use the ggplot() function to create a connected scatterplot with geom_point() and geom_line() for the data you created in Task 1.2.
Use the aes() function to map the year variable to the x-axis, the percent variable to the y-axis, and the varname_short variable to color and group aesthetic.
Use facet_wrap() to create a separate plot urban and rural populations.
Change the colors using scale_color_colorblind().
ggplot(data = sanitation_nigeria_urban_rural,
mapping = aes(x = year,
y = percent,
group = varname_short,
color = varname_short)) +
geom_point() +
geom_line() +
facet_wrap(~residence) +
scale_color_colorblind() 
library(readr)
library(dplyr)In this exercise we use data of the UNICEF/WHO Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (WASH). The data is available at https://washdata.org/data and published as an R data package at https://github.com/WASHNote/jmpwashdata/.
The data set jmp_wld_sanitation_long is available in the data folder of this repository. The data set is in long format and contains the following variables:
name: country nameiso3: ISO3 country codeyear: year of observationregion_sdg: SDG regionresidence: residence type (national, rural, urban)varname_short: short variable name (JMP naming convention)varname_long: long variable name (JMP naming convention)We use the read_csv() function to import the data set into R.
sanitation <- read_csv("/cloud/project/data/jmp_wld_sanitation_long.csv")Rows: 73710 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (6): name, iso3, region_sdg, varname_short, varname_long, residence
dbl (2): year, percent
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.glimpse() function to get an overview of the data set.glimpse(sanitation)Rows: 73,710
Columns: 8
$ name <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanista…
$ iso3 <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", …
$ year <dbl> 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 20…
$ region_sdg <chr> "Central and Southern Asia", "Central and Southern Asia"…
$ varname_short <chr> "san_bas", "san_bas", "san_bas", "san_lim", "san_lim", "…
$ varname_long <chr> "basic sanitation services", "basic sanitation services"…
$ residence <chr> "national", "rural", "urban", "national", "rural", "urba…
$ percent <dbl> 21.870802, 19.322798, 30.863719, 5.648528, 3.136148, 14.…count() function to identify how many SDG regions are included in the data set.sanitation |>
count(region_sdg)# A tibble: 8 × 2
region_sdg n
<chr> <int>
1 Australia and New Zealand 630
2 Central and Southern Asia 4410
3 Eastern and South-Eastern Asia 5670
4 Latin America and the Caribbean 15750
5 Northern America and Europe 16695
6 Oceania 6615
7 Sub-Saharan Africa 16065
8 Western Asia and Northern Africa 7875count() function to identify the levels in the varname_short and varname_long variables.sanitation |>
count(varname_short, varname_long)# A tibble: 5 × 3
varname_short varname_long n
<chr> <chr> <int>
1 san_bas basic sanitation services 14742
2 san_lim limited sanitation services 14742
3 san_od no sanitation facilities 14742
4 san_sm safely managed sanitation services 14742
5 san_unimp unimproved sanitation facilities 14742Use the filter() function to create a subset from the sanitation data containing national estimates for people with “no sanitation facilities” for the year 2020.
Store the result as a new object in your environment with the name sanitation_national_2020_od.
sanitation_national_2020_od <- sanitation |>
filter(residence == "national",
year == 2020,
varname_short == "san_od")sanitation_national_2020_od data and the count() function to identify the number of countries with 0% for the indicator “no sanitation facilities” in 2020.sanitation_national_2020_od |>
count(percent)# A tibble: 104 × 2
percent n
<dbl> <int>
1 0 96
2 0.00670 1
3 0.0107 1
4 0.0169 1
5 0.0317 1
6 0.0418 1
7 0.0965 1
8 0.100 1
9 0.105 1
10 0.127 1
# ℹ 94 more rowssanitation_national_2020_od data had no estimate for “no sanitation facilities” in 2020? Tipp: A country without an estimate has NA for the percent variable.sanitation_national_2020_od |>
filter(is.na(percent))# A tibble: 36 × 8
name iso3 year region_sdg varname_short varname_long residence percent
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
1 Anguilla AIA 2020 Latin Ame… san_od no sanitati… national NA
2 Antigua … ATG 2020 Latin Ame… san_od no sanitati… national NA
3 Argentina ARG 2020 Latin Ame… san_od no sanitati… national NA
4 Aruba ABW 2020 Latin Ame… san_od no sanitati… national NA
5 Azerbaij… AZE 2020 Western A… san_od no sanitati… national NA
6 Bahamas BHS 2020 Latin Ame… san_od no sanitati… national NA
7 Barbados BRB 2020 Latin Ame… san_od no sanitati… national NA
8 Bosnia a… BIH 2020 Northern … san_od no sanitati… national NA
9 British … VGB 2020 Latin Ame… san_od no sanitati… national NA
10 Brunei D… BRN 2020 Eastern a… san_od no sanitati… national NA
# ℹ 26 more rowsUse the sanitation_national_2020_od data in combination with group_by() and summarise() functions to calculate the mean, standard deviation and number of countries for the indicator “no sanitation facilities” in 2020.
How did you treat the missing values for the percent variable in the calculation?
sanitation_national_2020_od |>
filter(!is.na(percent)) |>
group_by(region_sdg) |>
summarise(
mean = mean(percent, na.rm = TRUE),
sd = sd(percent, na.rm = TRUE),
n = n()
)# A tibble: 8 × 4
region_sdg mean sd n
<chr> <dbl> <dbl> <int>
1 Australia and New Zealand 0 0 2
2 Central and Southern Asia 3.29 5.38 13
3 Eastern and South-Eastern Asia 5.16 6.97 16
4 Latin America and the Caribbean 2.04 3.70 34
5 Northern America and Europe 0.00698 0.0271 48
6 Oceania 6.80 13.1 16
7 Sub-Saharan Africa 19.4 18.4 47
8 Western Asia and Northern Africa 1.67 5.41 22library(readr)
library(dplyr)
library(ggplot2)
library(ggthemes)In this exercise we use data of the UNICEF/WHO Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (WASH). The data is available at https://washdata.org/data and published as an R data package at https://github.com/WASHNote/jmpwashdata/.
The data set jmp_wld_sanitation_long is available in the data folder of this repository. The data set is in long format and contains the following variables:
name: country nameiso3: ISO3 country codeyear: year of observationregion_sdg: SDG regionresidence: residence type (national, rural, urban)varname_short: short variable name (JMP naming convention)varname_long: long variable name (JMP naming convention)We use the read_csv() function to import the data set into R.
sanitation <- read_csv("/cloud/project/data/jmp_wld_sanitation_long.csv")Rows: 73710 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (6): name, iso3, region_sdg, varname_short, varname_long, residence
dbl (2): year, percent
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.glimpse() function to get an overview of the data set.glimpse(sanitation)Rows: 73,710
Columns: 8
$ name <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanista…
$ iso3 <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", …
$ year <dbl> 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 20…
$ region_sdg <chr> "Central and Southern Asia", "Central and Southern Asia"…
$ varname_short <chr> "san_bas", "san_bas", "san_bas", "san_lim", "san_lim", "…
$ varname_long <chr> "basic sanitation services", "basic sanitation services"…
$ residence <chr> "national", "rural", "urban", "national", "rural", "urba…
$ percent <dbl> 21.870802, 19.322798, 30.863719, 5.648528, 3.136148, 14.…count() function with varname_short and varname_long to identify the definitions of the levels in these two variables.sanitation |>
count(varname_short, varname_long)# A tibble: 5 × 3
varname_short varname_long n
<chr> <chr> <int>
1 san_bas basic sanitation services 14742
2 san_lim limited sanitation services 14742
3 san_od no sanitation facilities 14742
4 san_sm safely managed sanitation services 14742
5 san_unimp unimproved sanitation facilities 14742filter() function to create a subset of the data set that only contains observations:sanitation_uga <- sanitation |>
filter(iso3 == "UGA",
year %in% c(2000, 2020),
varname_short != "san_sm")count() function with the data you created in Task 3 to verify that year 2000 and 2020 remained in the year variable.sanitation_uga |>
count(year)# A tibble: 2 × 2
year n
<dbl> <int>
1 2000 12
2 2020 12Use the ggplot() function to create a bar plot with geom_col() for the data you created in Task 3.
Use the aes() function to map the residence variable to the x-axis, the percent variable to the y-axis, and the varname_long variable to the fill aesthetic.
Use facet_wrap() to create a separate plot for each year.
Change the fill colors using scale_fill_colorblind().
Add labels to the bars by copying the code below this bullet point and adding it to your code for the plot.
geom_text(aes(label = round(percent, 1)),
position = position_stack(vjust = 0.5),
size = 3,
color = "white") ggplot(data = sanitation_uga,
mapping = aes(x = residence,
y = percent,
fill = varname_long)) +
geom_col() +
facet_wrap(~year) +
scale_fill_colorblind() +
geom_text(aes(label = round(percent, 1)),
position = position_stack(vjust = 0.5),
size = 3,
color = "white") 
If you haven’t worked with JMP indicators before, the following questions will be challenging to answer.
sanitation_2020 <- sanitation |>
filter(year == 2020)
ggplot(data = sanitation_2020,
mapping = aes(x = percent, fill = varname_short)) +
geom_histogram() +
facet_grid(varname_short ~ residence, scales = "free_y") +
scale_fill_colorblind() +
theme(legend.position = "none") `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 1081 rows containing non-finite outside the scale range
(`stat_bin()`).
library(ggplot2)
library(dplyr)
library(readr)
library(ggthemes)waste <- read_csv("/cloud/project/data/processed/waste-city-level-sml.csv") Rows: 367 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): country, city, iso3c, income_id
dbl (2): generation_tons_year, population
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Run all code chunks above.
Use the glimpse() function to inspect the waste object.
What does the data cover? Briefly discuss with your room partner.
glimpse(waste)Rows: 367
Columns: 6
$ country <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afg…
$ city <chr> "Jalalabad", "Kandahar", "Mazar-E-Sharif", "Kabul…
$ iso3c <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AGO", "ALB", …
$ income_id <chr> "LIC", "LIC", "LIC", "LIC", "LIC", "LMC", "UMC", …
$ generation_tons_year <dbl> 58914.45, 120971.00, 52368.40, 1989250.00, 91644.…
$ population <dbl> 326585, 429000, 635250, 3700000, 337000, 4508000,…Use the count() function for the waste object to count the number of rows for each value of the income_id variable.
What do the four values of the income_id variable represent?
waste |>
count(income_id)# A tibble: 4 × 2
income_id n
<chr> <int>
1 HIC 88
2 LIC 74
3 LMC 124
4 UMC 81c() function to create a vector with the following values: “HIC”, “UMC”, “LMC”, “LIC”.<-) to store the resulting vector as a new object called levels_income.levels_income <- c("HIC", "UMC", "LMC", "LIC")Use the mutate() function to convert the income_id variable to a factor variable with the levels specified in the levels_income object.
Use the assignment operator (<-) to store the resulting data as a new object called waste_lvl.
waste_lvl <- waste |>
mutate(income_id = factor(income_id, levels = levels_income))count() function to verify that the income_id variable is now a factor variable with the correct levels.waste_lvl |>
count(income_id)# A tibble: 4 × 2
income_id n
<fct> <int>
1 HIC 88
2 UMC 81
3 LMC 124
4 LIC 74Starting with waste_lvl, use the mutate() function to create a new variable called generation_kg_capita that contains the generation_tons_year variable divided by the population variable and multiplied with 1000.
Use the assignment operator (<-) to store the resulting data as a new object called waste_capita.
waste_capita <- waste_lvl |>
mutate(generation_kg_capita = generation_tons_year / population * 1000) Next to the code chunk option #| eval: change the value from false to true.
Run the code in the code-chunk below to create a boxplot of the generation_kg_capita variable by income_id.
What do you observe? Discuss with your room partner.
ggplot(data = waste_capita,
mapping = aes(x = income_id,
y = generation_kg_capita,
color = income_id)) +
geom_boxplot(outlier.fill = NA) +
geom_jitter(width = 0.1, alpha = 0.3) +
scale_color_colorblind() +
labs(x = "Income group",
y = "Waste generation (tons per capita per year)")Warning: Removed 41 rows containing non-finite outside the scale range
(`stat_boxplot()`).Warning: Removed 41 rows containing missing values or values outside the scale range
(`geom_point()`).
library(readr)
library(readxl)
library(dplyr)read_csv() function to import the waste-city-level.csv file from the data/raw folder.waste.waste <- read_csv("/cloud/project/data/raw/waste-city-level.csv")Rows: 367 Columns: 113
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (41): iso3c, region_id, country_name, income_id, city_name, additional_d...
dbl (70): additional_data_annual_swm_budget_2017_year, additional_data_annua...
num (2): total_msw_total_msw_generated_tons_year, transportation_distance_k...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.read_csv() function to import the jmp_wld_sanitation_long.csv file from the data/processed folder.san_csv.san_csv <- read_csv("/cloud/project/data/processed/jmp_wld_sanitation_long.csv")Rows: 73710 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (6): name, iso3, region_sdg, varname_short, varname_long, residence
dbl (2): year, percent
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.read_rds() function to import the jmp_wld_sanitation_long.rds file from the data/processed folder.san_rds.san_rds <- read_rds("/cloud/project/data/processed/jmp_wld_sanitation_long.rds")glimpse() function to inspect the san_csv and san_rds objects.glimpse(san_csv)Rows: 73,710
Columns: 8
$ name <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanista…
$ iso3 <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", …
$ year <dbl> 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 20…
$ region_sdg <chr> "Central and Southern Asia", "Central and Southern Asia"…
$ varname_short <chr> "san_bas", "san_bas", "san_bas", "san_lim", "san_lim", "…
$ varname_long <chr> "basic sanitation services", "basic sanitation services"…
$ residence <chr> "national", "rural", "urban", "national", "rural", "urba…
$ percent <dbl> 21.870802, 19.322798, 30.863719, 5.648528, 3.136148, 14.…glimpse(san_rds)Rows: 73,710
Columns: 8
$ name <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanista…
$ iso3 <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", …
$ year <dbl> 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 20…
$ region_sdg <chr> "Central and Southern Asia", "Central and Southern Asia"…
$ varname_short <fct> san_bas, san_bas, san_bas, san_lim, san_lim, san_lim, sa…
$ varname_long <fct> basic sanitation services, basic sanitation services, ba…
$ residence <fct> national, rural, urban, national, rural, urban, national…
$ percent <dbl> 21.870802, 19.322798, 30.863719, 5.648528, 3.136148, 14.…You are an experienced educator in teaching R to novice users without prior knowledge. Explain what the .rds format is and how it differs from the .csv file format. Avoid technical language.
Read the answer and ask the tool questions for clarification of something is unclear.
Share a link to your conversation here (see screenshot below):
#Module 05:Conditions
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ lubridate 1.9.3 ✔ tibble 3.2.1
✔ purrr 1.0.2 ✔ tidyr 1.3.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggthemes)We continue to work with a subset of the “What a Waste” database.
waste <- read_csv("/cloud/project/data/processed/waste-city-level-sml.csv")Rows: 367 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): country, city, iso3c, income_id
dbl (2): generation_tons_year, population
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.We will also use an example spreadsheet that was created by one of the course participants.
solids <- readxl::read_excel("/cloud/project/data/raw/TS_poo_2022.xlsx")glimpse(waste)Rows: 367
Columns: 6
$ country <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afg…
$ city <chr> "Jalalabad", "Kandahar", "Mazar-E-Sharif", "Kabul…
$ iso3c <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AGO", "ALB", …
$ income_id <chr> "LIC", "LIC", "LIC", "LIC", "LIC", "LMC", "UMC", …
$ generation_tons_year <dbl> 58914.45, 120971.00, 52368.40, 1989250.00, 91644.…
$ population <dbl> 326585, 429000, 635250, 3700000, 337000, 4508000,…waste |>
count(income_id)# A tibble: 4 × 2
income_id n
<chr> <int>
1 HIC 88
2 LIC 74
3 LMC 124
4 UMC 81Conditional statements with mutate() & case_when() of dplyr R package
waste_cat <- waste |>
mutate(generation_kg_capita = generation_tons_year / population * 1000) |>
mutate(income_cat = case_when(
income_id == "HIC" ~ "high income",
income_id == "UMC" ~ "upper-middle income",
income_id == "LMC" ~ "lower-middle income",
income_id == "LIC" ~ "low income"
))
levels_income <- c("HIC", "UMC", "LMC", "LIC")
levels_income_cat <- c("high income",
"upper-middle income",
"lower-middle income",
"low income")
waste_fct <- waste_cat |>
mutate(income_id = factor(income_id, levels = levels_income)) |>
mutate(income_cat = factor(income_cat, levels = levels_income_cat)) |>
relocate(income_cat, .after = income_id)write_rds(x = waste_fct, file = "/cloud/project/data/processed/waste-city-level-sml.rds")solids |>
mutate(total_solids_gL = case_when(
source_type == "septic tank" ~ total_solids_gL * 100,
.default = total_solids_gL
))# A tibble: 20 × 5
source_location source_type Sample_Date n_daily_users total_solids_gL
<chr> <chr> <dttm> <dbl> <dbl>
1 household pit latrine 2022-11-01 00:00:00 5 20.5
2 household pit latrine 2022-11-01 00:00:00 7 25.8
3 household pit latrine 2022-11-01 00:00:00 7 22.6
4 household pit latrine 2022-11-01 00:00:00 6 30.9
5 household pit latrine 2022-11-01 00:00:00 8 48.3
6 household septic tank 2022-11-02 00:00:00 9 8
7 household septic tank 2022-11-02 00:00:00 6 11
8 household septic tank 2022-11-02 00:00:00 7 5
9 household septic tank 2022-11-02 00:00:00 7 13
10 household septic tank 2022-11-02 00:00:00 5 9
11 public toilet pit latrine 2022-11-03 00:00:00 35 35.0
12 public toilet pit latrine 2022-11-03 00:00:00 28 29.3
13 public toilet pit latrine 2022-11-03 00:00:00 52 19.9
14 public toilet pit latrine 2022-11-03 00:00:00 19 42.4
15 public toilet pit latrine 2022-11-03 00:00:00 39 28.0
16 public toilet septic tank 2022-11-04 00:00:00 75 7
17 public toilet septic tank 2022-11-04 00:00:00 53 14
18 public toilet septic tank 2022-11-04 00:00:00 47 19
19 public toilet septic tank 2022-11-04 00:00:00 39 9
20 public toilet septic tank 2022-11-04 00:00:00 62 11 ggplot(data = waste_cat,
mapping = aes(x = income_cat,
y = generation_kg_capita,
color = income_cat)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(size = 3, width = 0.1, alpha = 0.3) +
scale_color_colorblind() +
labs(x = "Income group",
y = "Waste generation (tons per capita per year)") Warning: Removed 41 rows containing non-finite outside the scale range
(`stat_boxplot()`).Warning: Removed 41 rows containing missing values or values outside the scale range
(`geom_point()`).
#| eval: true
ggplot(data = waste_fct,
mapping = aes(x = income_cat,
y = generation_kg_capita,
color = income_cat)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(size = 3, width = 0.1, alpha = 0.3) +
scale_color_colorblind() +
labs(x = "Income group",
y = "Waste generation (tons per capita per year)") Warning: Removed 41 rows containing non-finite outside the scale range
(`stat_boxplot()`).Warning: Removed 41 rows containing missing values or values outside the scale range
(`geom_point()`).
#Module 05a: Mutate()
library(tidyverse)
library(readxl)We are using another faecal sludge solids example dataset
sludge <- read_xlsx("/cloud/project/data/raw/faecal-sludge-analysis.xlsx")mutate() and case_when() to change the ts value of 0.72 to 8.72.sludge |>
mutate(ts = case_when(
ts == 0.72 ~ 8.72,
.default = ts
))# A tibble: 20 × 6
id date_sample system location users ts
<dbl> <dttm> <chr> <chr> <dbl> <dbl>
1 1 2023-11-01 00:00:00 pit latrine household 5 136.
2 2 2023-11-01 00:00:00 pit latrine household 7 102.
3 3 2023-11-01 00:00:00 pit latrine household NA 57.0
4 4 2023-11-01 00:00:00 pit latrine household 6 27.0
5 5 2023-11-01 00:00:00 pit latrine household 12 97.3
6 6 2023-11-02 00:00:00 pit latrine household 7 78.2
7 7 2023-11-02 00:00:00 septic tank household 14 15.2
8 8 2023-11-02 00:00:00 septic tank household 4 29.4
9 9 2023-11-02 00:00:00 septic tank household 10 64.2
10 10 2023-11-02 00:00:00 septic tank household 12 8.01
11 11 2023-11-03 00:00:00 pit latrine public toilet 50 11.2
12 12 2023-11-03 00:00:00 pit latrine public toilet 32 84.0
13 13 2023-11-03 00:00:00 pit latrine public toilet 41 55.9
14 14 2023-11-03 00:00:00 pit latrine public toilet 160 15.3
15 15 2023-11-03 00:00:00 pit latrine public toilet 20 22.6
16 16 2023-11-04 00:00:00 septic tank public toilet 26 8.72
17 17 2023-11-04 00:00:00 septic tank public toilet 91 43.9
18 18 2023-11-04 00:00:00 septic tank public toilet 68 10.4
19 19 2023-11-04 00:00:00 septic tank public toilet 112 23.2
20 20 2023-11-04 00:00:00 septic tank public toilet 59 15.6 mutate() and case_when() to change the system value of id 6 from “pit latrine” to “septic tank”.sludge |>
mutate(system = case_when(
id == 6 ~ "septic tank",
.default = system
))# A tibble: 20 × 6
id date_sample system location users ts
<dbl> <dttm> <chr> <chr> <dbl> <dbl>
1 1 2023-11-01 00:00:00 pit latrine household 5 136.
2 2 2023-11-01 00:00:00 pit latrine household 7 102.
3 3 2023-11-01 00:00:00 pit latrine household NA 57.0
4 4 2023-11-01 00:00:00 pit latrine household 6 27.0
5 5 2023-11-01 00:00:00 pit latrine household 12 97.3
6 6 2023-11-02 00:00:00 septic tank household 7 78.2
7 7 2023-11-02 00:00:00 septic tank household 14 15.2
8 8 2023-11-02 00:00:00 septic tank household 4 29.4
9 9 2023-11-02 00:00:00 septic tank household 10 64.2
10 10 2023-11-02 00:00:00 septic tank household 12 8.01
11 11 2023-11-03 00:00:00 pit latrine public toilet 50 11.2
12 12 2023-11-03 00:00:00 pit latrine public toilet 32 84.0
13 13 2023-11-03 00:00:00 pit latrine public toilet 41 55.9
14 14 2023-11-03 00:00:00 pit latrine public toilet 160 15.3
15 15 2023-11-03 00:00:00 pit latrine public toilet 20 22.6
16 16 2023-11-04 00:00:00 septic tank public toilet 26 0.72
17 17 2023-11-04 00:00:00 septic tank public toilet 91 43.9
18 18 2023-11-04 00:00:00 septic tank public toilet 68 10.4
19 19 2023-11-04 00:00:00 septic tank public toilet 112 23.2
20 20 2023-11-04 00:00:00 septic tank public toilet 59 15.6 ts_cat to the data frame that categorizes sludge samples into low, medium and high solids content.Use mutate() and case_when() to create the new variable.sludge |>
mutate(ts_cat = case_when(
ts < 15 ~ "low",
ts >= 15 & ts <= 50 ~ "medium",
ts > 50 ~ "high"
))# A tibble: 20 × 7
id date_sample system location users ts ts_cat
<dbl> <dttm> <chr> <chr> <dbl> <dbl> <chr>
1 1 2023-11-01 00:00:00 pit latrine household 5 136. high
2 2 2023-11-01 00:00:00 pit latrine household 7 102. high
3 3 2023-11-01 00:00:00 pit latrine household NA 57.0 high
4 4 2023-11-01 00:00:00 pit latrine household 6 27.0 medium
5 5 2023-11-01 00:00:00 pit latrine household 12 97.3 high
6 6 2023-11-02 00:00:00 pit latrine household 7 78.2 high
7 7 2023-11-02 00:00:00 septic tank household 14 15.2 medium
8 8 2023-11-02 00:00:00 septic tank household 4 29.4 medium
9 9 2023-11-02 00:00:00 septic tank household 10 64.2 high
10 10 2023-11-02 00:00:00 septic tank household 12 8.01 low
11 11 2023-11-03 00:00:00 pit latrine public toilet 50 11.2 low
12 12 2023-11-03 00:00:00 pit latrine public toilet 32 84.0 high
13 13 2023-11-03 00:00:00 pit latrine public toilet 41 55.9 high
14 14 2023-11-03 00:00:00 pit latrine public toilet 160 15.3 medium
15 15 2023-11-03 00:00:00 pit latrine public toilet 20 22.6 medium
16 16 2023-11-04 00:00:00 septic tank public toilet 26 0.72 low
17 17 2023-11-04 00:00:00 septic tank public toilet 91 43.9 medium
18 18 2023-11-04 00:00:00 septic tank public toilet 68 10.4 low
19 19 2023-11-04 00:00:00 septic tank public toilet 112 23.2 medium
20 20 2023-11-04 00:00:00 septic tank public toilet 59 15.6 medium#Module 05b: Dates
library(tidyverse)
library(readxl)dates <- read_excel("/cloud/project/data/raw/date-formats.xlsx")In R and other programming languages, dates are stored as numbers. The number of days since 1970-01-01 is the ISO 8601 standard.
In Excel, dates are stored as numbers of days since 1900-01-01. In Excel, the date number 1 corresponds to “1900-01-01,” but this system incorrectly considers 1900 as a leap year, which it is not. As a result, to correctly interpret date numbers that originate from systems like Excel, the origin “1899-12-30” is used to account for this discrepancy
dates_class <- dates |>
mutate(date_iso = as_date(date_iso)) |>
mutate(date_us = mdy(date_us)) |>
mutate(date_eu = dmy(date_eu)) |>
mutate(date_num = as_date(date_num, origin = "1899-12-30")) |>
mutate(date = as_date(date_time)) |>
mutate(date_time_tz = with_tz(date_time, tzone = "Africa/Kampala")) |>
mutate(today = today())
OlsonNames() [1] "Africa/Abidjan" "Africa/Accra"
[3] "Africa/Addis_Ababa" "Africa/Algiers"
[5] "Africa/Asmara" "Africa/Asmera"
[7] "Africa/Bamako" "Africa/Bangui"
[9] "Africa/Banjul" "Africa/Bissau"
[11] "Africa/Blantyre" "Africa/Brazzaville"
[13] "Africa/Bujumbura" "Africa/Cairo"
[15] "Africa/Casablanca" "Africa/Ceuta"
[17] "Africa/Conakry" "Africa/Dakar"
[19] "Africa/Dar_es_Salaam" "Africa/Djibouti"
[21] "Africa/Douala" "Africa/El_Aaiun"
[23] "Africa/Freetown" "Africa/Gaborone"
[25] "Africa/Harare" "Africa/Johannesburg"
[27] "Africa/Juba" "Africa/Kampala"
[29] "Africa/Khartoum" "Africa/Kigali"
[31] "Africa/Kinshasa" "Africa/Lagos"
[33] "Africa/Libreville" "Africa/Lome"
[35] "Africa/Luanda" "Africa/Lubumbashi"
[37] "Africa/Lusaka" "Africa/Malabo"
[39] "Africa/Maputo" "Africa/Maseru"
[41] "Africa/Mbabane" "Africa/Mogadishu"
[43] "Africa/Monrovia" "Africa/Nairobi"
[45] "Africa/Ndjamena" "Africa/Niamey"
[47] "Africa/Nouakchott" "Africa/Ouagadougou"
[49] "Africa/Porto-Novo" "Africa/Sao_Tome"
[51] "Africa/Timbuktu" "Africa/Tripoli"
[53] "Africa/Tunis" "Africa/Windhoek"
[55] "America/Adak" "America/Anchorage"
[57] "America/Anguilla" "America/Antigua"
[59] "America/Araguaina" "America/Argentina/Buenos_Aires"
[61] "America/Argentina/Catamarca" "America/Argentina/ComodRivadavia"
[63] "America/Argentina/Cordoba" "America/Argentina/Jujuy"
[65] "America/Argentina/La_Rioja" "America/Argentina/Mendoza"
[67] "America/Argentina/Rio_Gallegos" "America/Argentina/Salta"
[69] "America/Argentina/San_Juan" "America/Argentina/San_Luis"
[71] "America/Argentina/Tucuman" "America/Argentina/Ushuaia"
[73] "America/Aruba" "America/Asuncion"
[75] "America/Atikokan" "America/Atka"
[77] "America/Bahia" "America/Bahia_Banderas"
[79] "America/Barbados" "America/Belem"
[81] "America/Belize" "America/Blanc-Sablon"
[83] "America/Boa_Vista" "America/Bogota"
[85] "America/Boise" "America/Buenos_Aires"
[87] "America/Cambridge_Bay" "America/Campo_Grande"
[89] "America/Cancun" "America/Caracas"
[91] "America/Catamarca" "America/Cayenne"
[93] "America/Cayman" "America/Chicago"
[95] "America/Chihuahua" "America/Ciudad_Juarez"
[97] "America/Coral_Harbour" "America/Cordoba"
[99] "America/Costa_Rica" "America/Creston"
[101] "America/Cuiaba" "America/Curacao"
[103] "America/Danmarkshavn" "America/Dawson"
[105] "America/Dawson_Creek" "America/Denver"
[107] "America/Detroit" "America/Dominica"
[109] "America/Edmonton" "America/Eirunepe"
[111] "America/El_Salvador" "America/Ensenada"
[113] "America/Fort_Nelson" "America/Fort_Wayne"
[115] "America/Fortaleza" "America/Glace_Bay"
[117] "America/Godthab" "America/Goose_Bay"
[119] "America/Grand_Turk" "America/Grenada"
[121] "America/Guadeloupe" "America/Guatemala"
[123] "America/Guayaquil" "America/Guyana"
[125] "America/Halifax" "America/Havana"
[127] "America/Hermosillo" "America/Indiana/Indianapolis"
[129] "America/Indiana/Knox" "America/Indiana/Marengo"
[131] "America/Indiana/Petersburg" "America/Indiana/Tell_City"
[133] "America/Indiana/Vevay" "America/Indiana/Vincennes"
[135] "America/Indiana/Winamac" "America/Indianapolis"
[137] "America/Inuvik" "America/Iqaluit"
[139] "America/Jamaica" "America/Jujuy"
[141] "America/Juneau" "America/Kentucky/Louisville"
[143] "America/Kentucky/Monticello" "America/Knox_IN"
[145] "America/Kralendijk" "America/La_Paz"
[147] "America/Lima" "America/Los_Angeles"
[149] "America/Louisville" "America/Lower_Princes"
[151] "America/Maceio" "America/Managua"
[153] "America/Manaus" "America/Marigot"
[155] "America/Martinique" "America/Matamoros"
[157] "America/Mazatlan" "America/Mendoza"
[159] "America/Menominee" "America/Merida"
[161] "America/Metlakatla" "America/Mexico_City"
[163] "America/Miquelon" "America/Moncton"
[165] "America/Monterrey" "America/Montevideo"
[167] "America/Montreal" "America/Montserrat"
[169] "America/Nassau" "America/New_York"
[171] "America/Nipigon" "America/Nome"
[173] "America/Noronha" "America/North_Dakota/Beulah"
[175] "America/North_Dakota/Center" "America/North_Dakota/New_Salem"
[177] "America/Nuuk" "America/Ojinaga"
[179] "America/Panama" "America/Pangnirtung"
[181] "America/Paramaribo" "America/Phoenix"
[183] "America/Port_of_Spain" "America/Port-au-Prince"
[185] "America/Porto_Acre" "America/Porto_Velho"
[187] "America/Puerto_Rico" "America/Punta_Arenas"
[189] "America/Rainy_River" "America/Rankin_Inlet"
[191] "America/Recife" "America/Regina"
[193] "America/Resolute" "America/Rio_Branco"
[195] "America/Rosario" "America/Santa_Isabel"
[197] "America/Santarem" "America/Santiago"
[199] "America/Santo_Domingo" "America/Sao_Paulo"
[201] "America/Scoresbysund" "America/Shiprock"
[203] "America/Sitka" "America/St_Barthelemy"
[205] "America/St_Johns" "America/St_Kitts"
[207] "America/St_Lucia" "America/St_Thomas"
[209] "America/St_Vincent" "America/Swift_Current"
[211] "America/Tegucigalpa" "America/Thule"
[213] "America/Thunder_Bay" "America/Tijuana"
[215] "America/Toronto" "America/Tortola"
[217] "America/Vancouver" "America/Virgin"
[219] "America/Whitehorse" "America/Winnipeg"
[221] "America/Yakutat" "America/Yellowknife"
[223] "Antarctica/Casey" "Antarctica/Davis"
[225] "Antarctica/DumontDUrville" "Antarctica/Macquarie"
[227] "Antarctica/Mawson" "Antarctica/McMurdo"
[229] "Antarctica/Palmer" "Antarctica/Rothera"
[231] "Antarctica/South_Pole" "Antarctica/Syowa"
[233] "Antarctica/Troll" "Antarctica/Vostok"
[235] "Arctic/Longyearbyen" "Asia/Aden"
[237] "Asia/Almaty" "Asia/Amman"
[239] "Asia/Anadyr" "Asia/Aqtau"
[241] "Asia/Aqtobe" "Asia/Ashgabat"
[243] "Asia/Ashkhabad" "Asia/Atyrau"
[245] "Asia/Baghdad" "Asia/Bahrain"
[247] "Asia/Baku" "Asia/Bangkok"
[249] "Asia/Barnaul" "Asia/Beirut"
[251] "Asia/Bishkek" "Asia/Brunei"
[253] "Asia/Calcutta" "Asia/Chita"
[255] "Asia/Choibalsan" "Asia/Chongqing"
[257] "Asia/Chungking" "Asia/Colombo"
[259] "Asia/Dacca" "Asia/Damascus"
[261] "Asia/Dhaka" "Asia/Dili"
[263] "Asia/Dubai" "Asia/Dushanbe"
[265] "Asia/Famagusta" "Asia/Gaza"
[267] "Asia/Harbin" "Asia/Hebron"
[269] "Asia/Ho_Chi_Minh" "Asia/Hong_Kong"
[271] "Asia/Hovd" "Asia/Irkutsk"
[273] "Asia/Istanbul" "Asia/Jakarta"
[275] "Asia/Jayapura" "Asia/Jerusalem"
[277] "Asia/Kabul" "Asia/Kamchatka"
[279] "Asia/Karachi" "Asia/Kashgar"
[281] "Asia/Kathmandu" "Asia/Katmandu"
[283] "Asia/Khandyga" "Asia/Kolkata"
[285] "Asia/Krasnoyarsk" "Asia/Kuala_Lumpur"
[287] "Asia/Kuching" "Asia/Kuwait"
[289] "Asia/Macao" "Asia/Macau"
[291] "Asia/Magadan" "Asia/Makassar"
[293] "Asia/Manila" "Asia/Muscat"
[295] "Asia/Nicosia" "Asia/Novokuznetsk"
[297] "Asia/Novosibirsk" "Asia/Omsk"
[299] "Asia/Oral" "Asia/Phnom_Penh"
[301] "Asia/Pontianak" "Asia/Pyongyang"
[303] "Asia/Qatar" "Asia/Qostanay"
[305] "Asia/Qyzylorda" "Asia/Rangoon"
[307] "Asia/Riyadh" "Asia/Saigon"
[309] "Asia/Sakhalin" "Asia/Samarkand"
[311] "Asia/Seoul" "Asia/Shanghai"
[313] "Asia/Singapore" "Asia/Srednekolymsk"
[315] "Asia/Taipei" "Asia/Tashkent"
[317] "Asia/Tbilisi" "Asia/Tehran"
[319] "Asia/Tel_Aviv" "Asia/Thimbu"
[321] "Asia/Thimphu" "Asia/Tokyo"
[323] "Asia/Tomsk" "Asia/Ujung_Pandang"
[325] "Asia/Ulaanbaatar" "Asia/Ulan_Bator"
[327] "Asia/Urumqi" "Asia/Ust-Nera"
[329] "Asia/Vientiane" "Asia/Vladivostok"
[331] "Asia/Yakutsk" "Asia/Yangon"
[333] "Asia/Yekaterinburg" "Asia/Yerevan"
[335] "Atlantic/Azores" "Atlantic/Bermuda"
[337] "Atlantic/Canary" "Atlantic/Cape_Verde"
[339] "Atlantic/Faeroe" "Atlantic/Faroe"
[341] "Atlantic/Jan_Mayen" "Atlantic/Madeira"
[343] "Atlantic/Reykjavik" "Atlantic/South_Georgia"
[345] "Atlantic/St_Helena" "Atlantic/Stanley"
[347] "Australia/ACT" "Australia/Adelaide"
[349] "Australia/Brisbane" "Australia/Broken_Hill"
[351] "Australia/Canberra" "Australia/Currie"
[353] "Australia/Darwin" "Australia/Eucla"
[355] "Australia/Hobart" "Australia/LHI"
[357] "Australia/Lindeman" "Australia/Lord_Howe"
[359] "Australia/Melbourne" "Australia/North"
[361] "Australia/NSW" "Australia/Perth"
[363] "Australia/Queensland" "Australia/South"
[365] "Australia/Sydney" "Australia/Tasmania"
[367] "Australia/Victoria" "Australia/West"
[369] "Australia/Yancowinna" "Brazil/Acre"
[371] "Brazil/DeNoronha" "Brazil/East"
[373] "Brazil/West" "Canada/Atlantic"
[375] "Canada/Central" "Canada/Eastern"
[377] "Canada/Mountain" "Canada/Newfoundland"
[379] "Canada/Pacific" "Canada/Saskatchewan"
[381] "Canada/Yukon" "CET"
[383] "Chile/Continental" "Chile/EasterIsland"
[385] "CST6CDT" "Cuba"
[387] "EET" "Egypt"
[389] "Eire" "EST"
[391] "EST5EDT" "Etc/GMT"
[393] "Etc/GMT-0" "Etc/GMT-1"
[395] "Etc/GMT-10" "Etc/GMT-11"
[397] "Etc/GMT-12" "Etc/GMT-13"
[399] "Etc/GMT-14" "Etc/GMT-2"
[401] "Etc/GMT-3" "Etc/GMT-4"
[403] "Etc/GMT-5" "Etc/GMT-6"
[405] "Etc/GMT-7" "Etc/GMT-8"
[407] "Etc/GMT-9" "Etc/GMT+0"
[409] "Etc/GMT+1" "Etc/GMT+10"
[411] "Etc/GMT+11" "Etc/GMT+12"
[413] "Etc/GMT+2" "Etc/GMT+3"
[415] "Etc/GMT+4" "Etc/GMT+5"
[417] "Etc/GMT+6" "Etc/GMT+7"
[419] "Etc/GMT+8" "Etc/GMT+9"
[421] "Etc/GMT0" "Etc/Greenwich"
[423] "Etc/UCT" "Etc/Universal"
[425] "Etc/UTC" "Etc/Zulu"
[427] "Europe/Amsterdam" "Europe/Andorra"
[429] "Europe/Astrakhan" "Europe/Athens"
[431] "Europe/Belfast" "Europe/Belgrade"
[433] "Europe/Berlin" "Europe/Bratislava"
[435] "Europe/Brussels" "Europe/Bucharest"
[437] "Europe/Budapest" "Europe/Busingen"
[439] "Europe/Chisinau" "Europe/Copenhagen"
[441] "Europe/Dublin" "Europe/Gibraltar"
[443] "Europe/Guernsey" "Europe/Helsinki"
[445] "Europe/Isle_of_Man" "Europe/Istanbul"
[447] "Europe/Jersey" "Europe/Kaliningrad"
[449] "Europe/Kiev" "Europe/Kirov"
[451] "Europe/Kyiv" "Europe/Lisbon"
[453] "Europe/Ljubljana" "Europe/London"
[455] "Europe/Luxembourg" "Europe/Madrid"
[457] "Europe/Malta" "Europe/Mariehamn"
[459] "Europe/Minsk" "Europe/Monaco"
[461] "Europe/Moscow" "Europe/Nicosia"
[463] "Europe/Oslo" "Europe/Paris"
[465] "Europe/Podgorica" "Europe/Prague"
[467] "Europe/Riga" "Europe/Rome"
[469] "Europe/Samara" "Europe/San_Marino"
[471] "Europe/Sarajevo" "Europe/Saratov"
[473] "Europe/Simferopol" "Europe/Skopje"
[475] "Europe/Sofia" "Europe/Stockholm"
[477] "Europe/Tallinn" "Europe/Tirane"
[479] "Europe/Tiraspol" "Europe/Ulyanovsk"
[481] "Europe/Uzhgorod" "Europe/Vaduz"
[483] "Europe/Vatican" "Europe/Vienna"
[485] "Europe/Vilnius" "Europe/Volgograd"
[487] "Europe/Warsaw" "Europe/Zagreb"
[489] "Europe/Zaporozhye" "Europe/Zurich"
[491] "Factory" "GB"
[493] "GB-Eire" "GMT"
[495] "GMT-0" "GMT+0"
[497] "GMT0" "Greenwich"
[499] "Hongkong" "HST"
[501] "Iceland" "Indian/Antananarivo"
[503] "Indian/Chagos" "Indian/Christmas"
[505] "Indian/Cocos" "Indian/Comoro"
[507] "Indian/Kerguelen" "Indian/Mahe"
[509] "Indian/Maldives" "Indian/Mauritius"
[511] "Indian/Mayotte" "Indian/Reunion"
[513] "Iran" "Israel"
[515] "Jamaica" "Japan"
[517] "Kwajalein" "Libya"
[519] "MET" "Mexico/BajaNorte"
[521] "Mexico/BajaSur" "Mexico/General"
[523] "MST" "MST7MDT"
[525] "Navajo" "NZ"
[527] "NZ-CHAT" "Pacific/Apia"
[529] "Pacific/Auckland" "Pacific/Bougainville"
[531] "Pacific/Chatham" "Pacific/Chuuk"
[533] "Pacific/Easter" "Pacific/Efate"
[535] "Pacific/Enderbury" "Pacific/Fakaofo"
[537] "Pacific/Fiji" "Pacific/Funafuti"
[539] "Pacific/Galapagos" "Pacific/Gambier"
[541] "Pacific/Guadalcanal" "Pacific/Guam"
[543] "Pacific/Honolulu" "Pacific/Johnston"
[545] "Pacific/Kanton" "Pacific/Kiritimati"
[547] "Pacific/Kosrae" "Pacific/Kwajalein"
[549] "Pacific/Majuro" "Pacific/Marquesas"
[551] "Pacific/Midway" "Pacific/Nauru"
[553] "Pacific/Niue" "Pacific/Norfolk"
[555] "Pacific/Noumea" "Pacific/Pago_Pago"
[557] "Pacific/Palau" "Pacific/Pitcairn"
[559] "Pacific/Pohnpei" "Pacific/Ponape"
[561] "Pacific/Port_Moresby" "Pacific/Rarotonga"
[563] "Pacific/Saipan" "Pacific/Samoa"
[565] "Pacific/Tahiti" "Pacific/Tarawa"
[567] "Pacific/Tongatapu" "Pacific/Truk"
[569] "Pacific/Wake" "Pacific/Wallis"
[571] "Pacific/Yap" "Poland"
[573] "Portugal" "PRC"
[575] "PST8PDT" "ROC"
[577] "ROK" "Singapore"
[579] "SystemV/AST4" "SystemV/AST4ADT"
[581] "SystemV/CST6" "SystemV/CST6CDT"
[583] "SystemV/EST5" "SystemV/EST5EDT"
[585] "SystemV/HST10" "SystemV/MST7"
[587] "SystemV/MST7MDT" "SystemV/PST8"
[589] "SystemV/PST8PDT" "SystemV/YST9"
[591] "SystemV/YST9YDT" "Turkey"
[593] "UCT" "Universal"
[595] "US/Alaska" "US/Aleutian"
[597] "US/Arizona" "US/Central"
[599] "US/East-Indiana" "US/Eastern"
[601] "US/Hawaii" "US/Indiana-Starke"
[603] "US/Michigan" "US/Mountain"
[605] "US/Pacific" "US/Samoa"
[607] "UTC" "W-SU"
[609] "WET" "Zulu"
attr(,"Version")
[1] "2024a"as.numeric(today())[1] 19887as_date(1)[1] "1970-01-02"dates_class |>
select(today) |>
mutate(year = year(today)) |>
mutate(month = month(today, label = TRUE, abbr = FALSE, locale = "fr_FR")) |>
mutate(quarter = quarter(today)) |>
mutate(week = week(today)) |>
mutate(day = day(today)) |>
mutate(day_of_week = wday(today, label = TRUE, abbr = FALSE, locale = "fr_FR")) |>
mutate(day_of_year = yday(today)) |>
mutate(week_of_year = week(today)) Warning: There was 1 warning in `mutate()`.
ℹ In argument: `month = month(today, label = TRUE, abbr = FALSE, locale =
"fr_FR")`.
Caused by warning in `Sys.setlocale()`:
! OS reports request to set locale to "fr_FR" cannot be honored# A tibble: 1 × 9
today year month quarter week day day_of_week day_of_year
<date> <dbl> <ord> <int> <dbl> <int> <ord> <dbl>
1 2024-06-13 2024 June 2 24 13 Thursday 165
# ℹ 1 more variable: week_of_year <dbl>#Module 05c: Tables
library(tidyverse)
library(gt)
library(gtsummary)#Uighurlibrary(knitr)
library(DT)We continue to work with a subset of the “What a Waste” database.
waste_gt <- read_rds("/cloud/project/data/processed/waste-city-level-sml.rds")waste_tbl_income <- waste_gt |>
filter(!is.na(generation_kg_capita)) |>
group_by(income_cat) |>
summarise(
count = n(),
mean = mean(generation_kg_capita),
sd = sd(generation_kg_capita),
median = median(generation_kg_capita),
min = min(generation_kg_capita),
max = max(generation_kg_capita)
)waste_tbl_income# A tibble: 4 × 7
income_cat count mean sd median min max
<fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 high income 71 477. 214. 421. 116. 1142.
2 upper-middle income 72 381. 133. 378. 130. 828.
3 lower-middle income 116 275. 179. 219. 62.1 1109.
4 low income 67 215. 130. 182. 6.86 694.waste_tbl_income |>
gt() |>
tab_header(title = "Waste generation per capita (kg/year) by income group",
subtitle = "Data from 326 cities") |>
fmt_number(columns = count:max, decimals = 0) |>
cols_label(income_cat = "income category")| Waste generation per capita (kg/year) by income group | ||||||
|---|---|---|---|---|---|---|
| Data from 326 cities | ||||||
| income category | count | mean | sd | median | min | max |
| high income | 71 | 477 | 214 | 421 | 116 | 1,142 |
| upper-middle income | 72 | 381 | 133 | 378 | 130 | 828 |
| lower-middle income | 116 | 275 | 179 | 219 | 62 | 1,109 |
| low income | 67 | 215 | 130 | 182 | 7 | 694 |
Table 1 highlights that cities in countries classfied as high income countries generate more waste per capita than cities in lower income countries.
waste_tbl_income |>
rename(`income category` = income_cat) |>
kable(digits = 0)| income category | count | mean | sd | median | min | max |
|---|---|---|---|---|---|---|
| high income | 71 | 477 | 214 | 421 | 116 | 1142 |
| upper-middle income | 72 | 381 | 133 | 378 | 130 | 828 |
| lower-middle income | 116 | 275 | 179 | 219 | 62 | 1109 |
| low income | 67 | 215 | 130 | 182 | 7 | 694 |
library(tidyverse)
library(ggthemes)
library(palmerpenguins)
library(gt)Render the document and identify if the cross-reference to the table generated from the code below works.
Fix the label in the code-chunk below so that the cross-reference works.
Render the document to check if the cross-reference to the table generated from the code below works
See Table 2 for data on a few penguins.
penguins |>
filter(!is.na(bill_depth_mm)) |>
group_by(island, species) |>
summarise(n = n(),
mean_bill_depth = mean(bill_depth_mm),
sd_bill_depth = sd(bill_depth_mm)) |>
ungroup() |>
gt() |>
fmt_number(columns = c(mean_bill_depth, sd_bill_depth),
decimals = 1)`summarise()` has grouped output by 'island'. You can override using the
`.groups` argument.| island | species | n | mean_bill_depth | sd_bill_depth |
|---|---|---|---|---|
| Biscoe | Adelie | 44 | 18.4 | 1.2 |
| Biscoe | Gentoo | 123 | 15.0 | 1.0 |
| Dream | Adelie | 56 | 18.3 | 1.1 |
| Dream | Chinstrap | 68 | 18.4 | 1.1 |
| Torgersen | Adelie | 51 | 18.4 | 1.3 |
In Figure 1, we see that …
ggplot(data = penguins,
mapping = aes(x = bill_length_mm,
y = bill_depth_mm,
color = species,
shape = species)) +
geom_point() +
scale_color_colorblind() +
labs(x = "Bill length (mm)", y = "Bill depth (mm)") +
theme_minimal()Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
#Module 06b: Vector types”
library(tidyverse)
library(gapminder)Atomic vectors, of which there are six types: logical, integer, double, character, complex, and raw.
Integer and double vectors are collectively known as numeric vectors.
glimpse(gapminder)Rows: 1,704
Columns: 6
$ country <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", …
$ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, …
$ year <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, …
$ lifeExp <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.8…
$ pop <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12…
$ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, …vector_lgl <- c(TRUE, TRUE, FALSE)
typeof(vector_lgl)[1] "logical"sum(vector_lgl)[1] 2as.numeric(vector_lgl)[1] 1 1 0vector_int <- c(1L, 3L, 6L)
typeof(vector_int)[1] "integer"vector_dbl <- c(1293, 5.1, 90.5)
typeof(vector_dbl)[1] "double"vector_chr <- c("large", "small", "medium")
typeof(vector_chr)[1] "character"vector_dbl > 150[1] TRUE FALSE FALSE"large" == vector_chr[1] TRUE FALSE FALSEstr_detect(vector_chr, "lar")[1] TRUE FALSE FALSEVectors can also contain arbitrary additional metadata in the form of attributes. These attributes are used to create augmented vectors which build on additional behavior. For example, factors are built on top of integer vectors.
vector_fct <- factor(vector_chr, levels = c("small", "medium", "large"))
typeof(vector_fct)[1] "integer"attributes(vector_fct)$levels
[1] "small" "medium" "large"
$class
[1] "factor"as.integer(vector_fct)[1] 3 1 2Tibbles / dataframes have vectors as columns. Each vector has the same length. Each vector can be thought of as a column and the length of each vector is the number of rows.
tib_data <- tibble(
vector_lgl,
vector_int,
vector_dbl,
vector_chr,
vector_fct,
date = Sys.Date()
)tib_data |>
pull(vector_fct)[1] large small medium
Levels: small medium largetib_data$vector_fct[1] large small medium
Levels: small medium largetib_data[5]# A tibble: 3 × 1
vector_fct
<fct>
1 large
2 small
3 medium tib_data[[5]][1] large small medium
Levels: small medium largeIterate code for each element in a vector.
size <- tib_data$vector_fct
for (s in size) {
msg <- paste(
"------", s, "------"
)
print(msg)
}[1] "------ large ------"
[1] "------ small ------"
[1] "------ medium ------"pet <- c("bat", "cat", "dog", "bird", "horse")
for(p in pet) {
if(p == "dog") {
msg <- paste("A", p, "is the best!")
} else {
msg <- paste("A", p, "is okay I guess.")
}
print(msg)
}[1] "A bat is okay I guess."
[1] "A cat is okay I guess."
[1] "A dog is the best!"
[1] "A bird is okay I guess."
[1] "A horse is okay I guess."sounds <- c(NA, "meow", "woof", "chirp", "neigh")
for (i in seq_along(pet)) {
if (pet[i] == "dog") {
message <- paste("The", pet[i], "goes", sounds[i])
} else {
message <- paste("The", pet[i], "says", sounds[i])
}
print(message)
}[1] "The bat says NA"
[1] "The cat says meow"
[1] "The dog goes woof"
[1] "The bird says chirp"
[1] "The horse says neigh"#Module 06c: Excercises
library(tidyverse)
library(nycflights13)Create a numeric vector using c() with the numbers from 1, 2, 3, 4, 5 to 10. Run the code.
Create a numeric vector using seq(1, 10) and run the code.
What’s the difference between the two vectors?
c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) [1] 1 2 3 4 5 6 7 8 9 10seq(1, 10) [1] 1 2 3 4 5 6 7 8 9 10Create a character vector using c() with the letters from “a” to “f”. Run the code.
On a new line, write letters and run the code. What’s stored in the letters object?
On a new line, write ?letters and run the code. What did you learn?
c("a", "b", "c", "d", "e", "f")[1] "a" "b" "c" "d" "e" "f"letters [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
[20] "t" "u" "v" "w" "x" "y" "z"Create a numeric vector using seq(1, 100, 1) and run the code. What does the code do?
Create a numeric vector using runif(100, 1, 100) and run the code. What does the code do?
Create a numeric vector using sample(1:100, 100, replace = FALSE) and run the code. What does the code do?
seq(1, 100, 1) [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
[19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
[37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
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[73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
[91] 91 92 93 94 95 96 97 98 99 100runif(100, 1, 100) [1] 62.988709 16.726003 75.236663 80.045643 68.887587 53.002665 6.737848
[8] 4.149490 72.590134 25.511785 28.485045 16.904528 35.253018 2.480283
[15] 20.854578 49.939844 86.222353 80.697115 44.409734 43.814583 69.314222
[22] 66.888119 91.970093 60.807513 58.267554 68.078825 44.931375 4.557237
[29] 3.970521 26.930084 73.004838 87.254465 63.858116 16.715693 4.468917
[36] 5.926754 16.984858 86.029451 22.340624 20.551831 10.243461 82.360416
[43] 56.374376 16.898205 79.684386 3.097537 25.146377 47.084229 70.431434
[50] 48.720182 41.950871 43.960712 13.307139 94.894827 38.783436 60.053176
[57] 77.664385 75.504480 11.883027 70.827453 20.486788 44.730924 81.062765
[64] 77.732577 34.598610 50.167204 41.357988 12.909231 47.073104 11.486103
[71] 4.640029 8.359456 81.653073 48.524680 76.294791 28.403828 95.728214
[78] 86.858965 36.170893 31.854923 63.811384 58.414581 72.527839 88.947286
[85] 38.399763 37.226736 48.085935 91.734532 25.327156 76.343979 84.618772
[92] 58.129356 96.864793 71.805724 50.833802 43.456725 50.391842 30.248384
[99] 40.519638 93.173912sample(1:100, 100, replace = FALSE) [1] 100 46 52 99 8 64 41 6 50 7 82 12 94 32 19 39 93 21
[19] 59 65 37 68 24 55 1 45 66 33 36 98 61 20 54 67 14 22
[37] 53 2 51 40 10 18 92 42 81 91 43 83 84 80 44 70 69 79
[55] 25 48 78 49 15 60 13 87 23 57 3 90 29 4 73 16 31 76
[73] 97 35 58 56 28 34 85 30 17 89 96 75 11 86 74 9 71 72
[91] 47 38 95 88 27 62 26 63 5 77seq_along(letters) [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26Create a numeric vector using seq_along(letters) and run the code. What does the code do?
Create a character vector using month.name and run the code. What does the code do?
Create a numeric vector using seq_along(month.name) and run the code. What does the code do?
seq_along(letters) [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26month.name [1] "January" "February" "March" "April" "May" "June"
[7] "July" "August" "September" "October" "November" "December" seq_along(month.name) [1] 1 2 3 4 5 6 7 8 9 10 11 12Create a numeric vector runif(n = 1000, min = 1, max = 100) |> hist() and run the code. What does the code do? Remove |> hist() and run the code again. What does the code do?
Create a numeric vector rnorm(n = 1000, mean = 500, sd = 150) |> hist() and run the code. What does the code do? Remove |> hist() and run the code again. What does the code do?
runif(n = 1000, min = 1, max = 100) |> hist()
rnorm(n = 1000, mean = 500, sd = 150) |> hist()
Create a numeric vector using rnorm(n = 1000, mean = 50, sd = 5) and use the assignment operator to store it in an object called norm100. Run the code.
Write:
mean(norm100) and run the code. What does the code do?norm100 >= 50 and run the code. What does the code do?sum(norm100 >= 50) and run the code. What does the code do?mean(norm100 >= 50) and run the code. What does the code do?norm_dist <- rnorm(n = 1000, mean = 50, sd = 5)
mean(norm_dist)[1] 50.07664norm_dist >= 50 [1] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE
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[997] FALSE FALSE TRUE FALSEsum(norm_dist >= 50)[1] 499mean(norm_dist >= 50)[1] 0.499In this task, we will use the flights data object of the nycflights13 package. The flights data object contains information about all flights that departed from NYC (e.g., EWR, JFK and LGA) in 2013. The data object contains 336,776 rows and 19 columns.
Use the flights data object with mutate() to create delayed, a variable that displays whether a flight was delayed (arr_delay > 0).
Use relocate to move delayed to the front of the data frame. Run the code. What vector type is the delayed variable?
Then, remove all rows that contain an NA in delayed.
Finally, create a summary table with summarise() that shows
flights |>
mutate(delayed = arr_delay > 0) |>
relocate(delayed) |>
filter(!is.na(delayed)) |>
summarise(sum = sum(delayed),
prop = mean(delayed))# A tibble: 1 × 2
sum prop
<int> <dbl>
1 133004 0.406The required packages for this homework exercise have already been added.
tidyverse Package? Describe in maximum two sentences below.
The tidyverse is a collection of R packages designed for data science that share a common design philosophy, grammar, and data structures. It provides a powerful and coherent system for working with data, including tools for data import, tidying, manipulation, visualization, and programming.[1][3]
Citations: [1] https://jhudatascience.org/tidyversecourse/intro.html [2] https://www.tmwr.org/tidyverse [3] https://tidyverse.tidyverse.org/articles/paper.html [4] https://rafalab.dfci.harvard.edu/dsbook-part-1/R/tidyverse.html [5] https://www.datacamp.com/tutorial/tidyverse-tutorial-r
Soruce: https://www.perplexity.ai/search/describe-the-tidyverse-JEwTv1xJTvOFRgullFskTQ
library(tidyverse)read_csv() (Note: Watch out for the _ and don’t use the . as in read.csv()) function to import the “msw-generation-and-composition-by-income.csv” data from the data directory and assign it to an object with the name waste_data.waste_data = read_csv(file = "/cloud/project/data/msw-generation-and-composition-by-income.csv")Rows: 217 Columns: 15
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): country_name, income_id, income_cat
dbl (12): population, msw_tons_year, msw_capita_kg_year, food_organic_waste,...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Use waste_data and count() to create a frequency table for the income_cat variable.
Use then c() function to create a vector with a sensible order for the the values in income_cat. Use the assignment operator <- to assign the vector to an object with the name levels_income_cat.
Starting with the waste_data object, use the pipe operator and the mutate() function to convert the income_cat variable from a variable of type character to a variable of type factor. Use the levels you defined in the previous step.ories using the following code to identify the correct spelling of the categories in the variable income_cat.
Assign the created data frame to an object with the name waste_data_fct.
Render and fix any errors
# Create a frequency table for the income_cat variable
waste_data %>%
count(income_cat)# A tibble: 4 × 2
income_cat n
<chr> <int>
1 high income 81
2 low income 33
3 lower-middle income 47
4 upper-middle income 56#Create vector with a sensible order for the values in income_cat
levels_income_cat <- c(
"Low income",
"Medium income",
"High income"
)
#Conversion of data type
waste_data_fct <- waste_data %>%
mutate(income_cat = factor(income_cat, levels = levels_income_cat))waste_data_fct object, use the pivot_longer() function to convert the data frame from a wide to a long format. Apply the following:food_organic_waste to yard_garden_green_waste into a long formatRemove all NAs from the percent variable
Assign the created data frame to an object with the name waste_data_long
Render and fix any errors
waste_data_long <- waste_data_fct %>%
pivot_longer(cols = food_organic_waste:yard_garden_green_waste,
names_to = "waste_category",
values_to = "percent") %>%
filter(!is.na(percent)) # Remove rows where percent column is NAI have stored the data that I would have expected at the end of the previous task and import it here.
waste_data_long <- read_rds("/cloud/project/data/msw-generation-and-composition-by-income-long.rds")Starting with waste_data_long, group the data byincome_cat and waste_category, then create a summary table containing the mean of percentages (call this mean_percent) for each group.
Assign the created data frame to an object with the name waste_data_long_mean.
waste_data_long_mean <- waste_data_long %>%
group_by(income_cat, waste_category) %>%
summarise(mean_percent = mean(percent))`summarise()` has grouped output by 'income_cat'. You can override using the
`.groups` argument.waste_data_long_mean object, execute the code and observe the output in the Console. Would you publish this table in this format in a report?
waste_data_long_mean# A tibble: 36 × 3
# Groups: income_cat [4]
income_cat waste_category mean_percent
<fct> <chr> <dbl>
1 high income food_organic_waste 32.8
2 high income glass 6.12
3 high income metal 5.13
4 high income other 16.8
5 high income paper_cardboard 21.3
6 high income plastic 12.4
7 high income rubber_leather 2.98
8 high income wood 5.54
9 high income yard_garden_green_waste 9.31
10 upper-middle income food_organic_waste 45.4
# ℹ 26 more rowsStarting with the waste_data_long_mean object, use the pipe operator to add another line of code which uses the pivot_wider() function to bring the data from a long format into a wide format using names for variables from waste_category and corresponding values from mean_percent
Execute the code and observe the output in the Console. Would you publish this table in a report in this format?
Render and fix any errors
waste_data_long_mean %>%
pivot_wider(names_from = waste_category,
values_from = mean_percent)# A tibble: 4 × 10
# Groups: income_cat [4]
income_cat food_organic_waste glass metal other paper_cardboard plastic
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 high income 32.8 6.12 5.13 16.8 21.3 12.4
2 upper-middle inc… 45.4 4.42 3.88 18.2 12.1 12.3
3 lower-middle inc… 50.4 3.68 3.92 16.5 10.6 11.1
4 low income 50.9 1.94 2.58 28.5 8.35 7.97
# ℹ 3 more variables: rubber_leather <dbl>, wood <dbl>,
# yard_garden_green_waste <dbl>I have stored the data that I would have expected at the end of the previous task and import it here.
waste_data_long_mean <- read_rds("/cloud/project/data/msw-generation-and-composition-by-income-long-mean.rds")Render and fix any errors.
Reproduce the plot that you see as an image below when you render the report and view the output in your Viewer tab in the bottom right window.
Hint: To get those bars displayed next to each other, use the geom_col() function and apply the position = position_dodge() argument and value. The colors don’t have to be exactly the same colors, just not the default color scale
Note: The size of the plot will be different. That is alright and does not need to match.
ggplot(data = waste_data_long_mean,
aes(x = mean_percent,
y = waste_category,
fill = income_cat)) +
geom_col(position = position_dodge(0.9), na.rm = TRUE) +
labs(x = "Income Category", y = "Mean Percent", fill = "Waste Category") +
theme_minimal()
#Module 07: Writing scholarly articles
Scholarly articles require much more detail in their front matter than simply a title and an author. Quarto provides a rich set of YAML metadata keys to describe these details. You can copy & paste from this example to your own report.
In @tilley2021my, the authors describe how visitors still expect a personal pick-up, despite the availability of taxi services.
Inequality underpins waste management systems, structuring who can or cannot access services [@kalina2023rich].
Your folder already contains a references.bib file. One way of creating and adding to this file is by using the RStudio Visual Editor mode. Another way is by exporting a collection in Zotero reference management tool. Part of your homework will be to setup Zotero. For your literature research, you will then use Zotero and in your final report use an exported .bib file to cite references in your report. https://rbtl-fs24.github.io/website/project